Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTOMATION CAPABILITIES ANALYSIS METHODOLOGY FOR NON-CONTROLLED AIRPORTS

Advanced technologies will be needed to enhance and improve the current transportation capabilities of the nation's small aircraft transportation network, and thus help relieve congestion at hub airports. To achieve this goal, current operation concepts and procedures will have to be changed and improved, or perhaps even replaced. This paper proposes a decentralized Air Traffic Management approach, centered on the design of an automated arrival/departure system for non-controlled airports. A hybrid system is introduced that is a fusion of distributed air traffic management and centralized control. A new functional description of the airport terminal area infrastructure named Multi-layer Air Traffic Space is introduced, and automated terminal operations and procedures are addressed. Several types of intelligent agents with negotiation functions are developed in the automation system, and simulation methodology is presented, containing a full hardware and software description.
The Clinical Impact of an Early Decline in Kidney Function in Patients Following Heart Transplantation

Renal dysfunction is a well‐known complication following heart transplantation. We examined an early decline in kidney function as a predictor of progression to end‐stage renal disease and mortality in heart transplant recipients. We performed a retrospective cohort study of 233 patients who received a heart transplant between July 1985 and July 2004, and who survived >1 month. The decline in estimated creatinine clearance (CrCl) was used to predict the outcomes of need for chronic dialysis or mortality >1‐year posttransplant. The earliest time to chronic dialysis was 484 days. A 30% decline in CrCl between 1 month and 12 months predicted the need for chronic dialysis (p = 0.01), all‐cause mortality (p < 0.0001) and time to first CrCl ≤30 mL/min at >1‐year posttransplant (p = 0.02). A 30% decline in CrCl between 1 month and 3 months also independently predicted the need for chronic dialysis (p = 0.04) and time to first CrCl ≤ 30 mL/min at >1‐year posttransplant (p = 0.01). In conclusion, an early drop in CrCl within the first year is a strong predictor of chronic dialysis and death >1‐year postheart transplantation. Future studies should focus on kidney function preservation in those identified at high risk for progression to end‐stage kidney disease and mortality.
Dimensioning and Profit Sharing in Hybrid LTE/DVB Systems to Offer Mobile TV Services

Our aim in this work is to develop profit sharing strategies for coooperative 4G Long Term Evolution (LTE) and Digital Video Broadcasting - Next Generation Handheld (DVB-NGH) systems offering mobile TV service. We first study the capacity of the hybrid system for offering such a service and derive the associated cost for each network. We then focus on the sharing of profit between LTE and DVB operators as well as TV channels providers, taking into account the subscription revenues as well as the infrastructure and operation costs. We consider two cases: one in which both LTE and DVB networks are managed by a single operator and one in which the operators are separate. In both cases, we derive closed-form expressions for each player profit share using coalition game concept Shapley value. We further obtain the players optimal strategies, at the Nash equilibrium, where each player tries to maximize its own profit.
High Impact The Role of Trehalose Biosynthesis in Plants

The role of trehalose in plants is a curious one. Originally believed to serve as an osmoprotectant, its presence in plants at very low levels makes this role unlikely. This month’s High Impact article by Avonce et al., titled ‘‘The Arabidopsis Trehalose-6-P Synthase AtTPS1 Gene Is a Regulator of Glucose, Abscisic Acid, and Stress Signaling,’’ appeared in our November 2004 issue and adds to the growing body of evidence that it is not trehalose itself but more likely a synthesis pathway intermediate or perhaps an enzyme of the synthesis pathway that plays a key role in plants.
CrowdMiner: Mining association rules from the crowd

This demo presents CrowdMiner, a system enabling the mining of interesting data patterns from the crowd. While traditional data mining techniques have been used extensively for finding patterns in classic databases, they are not always suitable for the crowd, mainly because humans tend to remember only simple trends and summaries rather than exact details. To address this, CrowdMiner employs a novel crowd-mining algorithm, designed specifically for this context. The algorithm iteratively chooses appropriate questions to ask the crowd, while aiming to maximize the knowledge gain at each step. We demonstrate CrowdMiner through a Well-Being portal, constructed interactively by mining the crowd, and in particular the conference participants, for common health related practices and trends.
Evolutionary fuzzy control of two cooperative object-carrying wheeled robots for wall following through multiobjective continuous ACO

This paper considers the multiobjective orientation and velocity control of two robots cooperatively carrying an object in completing a convex wall-following task. Fuzzy controllers (FCs) found through a two-stage learning approach is used. The first stage finds FCs that control a single (leader) robot in executing the wall-following task with the two objectives of accurately moving along the wall boundary and moving with a high speed. Based on the FC in stage one, the second stage finds an auxiliary FC to control a follower robot so that the leader and follower robots can cooperatively carry an object in executing the wall-following task. The FC in each stage is optimized through the multiobjective front-guided continuous ant-colony optimization (MO-FCACO) algorithm. Simulations of evolutionary fuzzy control of a single robot and two cooperative robots for wall following are performed to show the control result.
Psychological Stress and Coping in Recently Discharged Postsurgical Cancer Patients

Objective: Cancer patients and survivors need to cope with various stressful situations and problems even after treatment. In this study, we sought to investigate psychological stress and coping in recently discharged postsurgical cancer patients. Methods: A mail-in questionnaire survey about stress response, perceived illness-related demands, and coping strategies and styles was administered to postsurgical Japanese cancer patients. The questionnaires were returned a week after the patients′ discharge from the hospital. Descriptive and nonparametric statistical analyses were used. Results: Forty-two patients completed the questionnaire; their average age was 58.1 years, and 61.9% were female. The stress response scale-18 (SRS-18) score was lower than that reported among the general population. The proportion of patients who were concentrating coping on social support or positive reappraisal was high. The scores for problem- and emotion-focused coping were nearly identical. SRS-18 scores were weakly correlated with those for emotion-focused coping (r = 0.38, P = 0.014). The demographic data were not significantly associated with any of the stress or coping variables. However, SRS-18 scores for patients who had adjuvant therapy and physical, functional disorders were significantly higher than those for patients who did not (P = 0.004 and P = 0.008, respectively). Conclusions: Most of the patients had a low-stress response and used appropriate coping strategies. However, the findings suggest that attention must be paid to stress-coping in patients who have a physical, functional disorder as well as in those receiving adjuvant therapy.
Video Structural Description: A Semantic Based Model for Representing and Organizing Video Surveillance Big Data

Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Especially, the data volume of all video surveillance devices in Shanghai, China is up to 1 TB every day. Thus, it is important to accurately describe the video content and enable the organizing and searching potential videos in order to detect and analyze related traffic events. Unfortunately, raw data and low level features cannot meet the video based tasks. In this paper, we propose a semantic based model for representing and organizing video big data. The proposed method defines a number of concepts and their relations, which allow users to use them to annotate video traffic events. The defined concepts including people, vehicle, and traffic sigh, which can be used by users for annotating and representing video traffic events unambiguous. In addition, we define the spatial and temporal relations in event and concepts definitions, which can be used by users for annotating and representing the semantic relations between objects in video traffic events. Moreover, semantic link network is used for organizing video resources based on their associations. In the application, we illustrate two systems using the proposed method for annotating and searching video resources.
A MODEL ORDER AND TIME-DELAY SELECTION METHOD FOR MIMO NON-LINEAR SYSTEMS AND IT ’ S APPLICATION TO NEURAL MODELLING

A new model order and time-delay selection method for neural network modelling of SISO non-linear systems has been recently proposed. The extension of this method to the MIMO case is developed in this paper. The MIMO form of the NARX model is considered and the order and time-delay for each input are selected by identifying linearised models of the system. Application of the method to a simulated continuously stirred tank reactor (CSTR) process is investigated to demonstrate the selection procedure. Neural models are subsequently developed for the process based on the order and time-delay selected using the proposed method and are compared to other neural models with different structures to demonstrate the effectiveness of the method.
Optimal component selection for image segmentation via Parallel Analysis

In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis (PA). Besides, Local Standard Deviation (LSD) which is a method for extracting meaningful sub-features is applied to whole image for successful segmentation. The effect of segmentation success rate is analyzed by selected features. Consequently, a novel algorithm is proposed for minimizing total computation time and error of dimension reduction significantly. It is seen that the results of the algorithm are approximately same as conventional segmentation algorithms.
Dictionary learning based pan-sharpening

Pan-sharpening is an image fusion process in which high resolution (HR) panchromatic (Pan) imagery is used to sharpen the corresponding low resolution (LR) multi-spectral (MS) imagery. Pan-sharpened MS images generally have high spatial resolutions, but exhibit color distortions. In this paper, we propose a dictionary learning based pan-sharpening process to reduce the color distortion caused by the interpolation of the MS imagery. Instead of interpolating the LR MS image before fusion, we generate an improved MS image which is sparse with respect to a dictionary learned from the image data. Our experiments on degraded QuickBird and IKONOS images demonstrate that the distortion in the MS images produced using our approach is significantly reduced.
The effect of breastfeeding on spontan resolution of monosymptomatic enuresis

ABSTRACT Purpose The aim of this study was to examine whether the duration of breastfeeding during infancy was associated with the time of spontaneous resolution of monosymptomatic enuresis (SRME). Materials and Methods A total of 1500 people were surveyed at four centers. One hundred and eighty-one people with a history of monosymptomatic enuresis (ME) who received no treatment and had no day time symptoms were included in the study. The relationship between the duration of breastfeeding and SRME was assessed by considering the duration of breastfeeding as both continuous and categorical (cut-off value 5 months) variable. The multivariate general linear model was used to identify independent predictors such as gender, family history, and educational status of parents. Results Pearson correlation analysis of the age of SRME and duration of breastfeeding found no statistically significant relationship. However, there was a significant difference in the age of SRME of those who were breastfed for 5 months or less compared to those who were breastfed for more than 5 months. According to the multivariate analysis, gender and educational status of parents were not effective on the age of SRME. Stepwise linear regression model showed that breastfeeding for five months or less and family history could affect the age of SRME. The regression formula was: age of SRME=9.599 + (3.807×five months or less of breastfeeding) + (1.258×positive family history). Conclusions It was found that when breastfeeding lasted for more than 5 months, there was a positive contribution to SRME.
Crescita post-traumatica: un'opportunità dopo il trauma?

Riassunto La letteratura sul trauma ha evidenziato ampiamente le conseguenze psicopatologiche sulla vittima. Tuttavia fra le reazioni post-traumatiche vi e anche la possibilita di esperire una crescita in termini personali e interpersonali. Lo scopo di questa ricerca e di verificarne l’esistenza, l’entita e le relazioni con altre variabili fra cui il sostegno sociale, attivita di coping ed emozioni sperimentate dopo il trauma. Il campione e composto da 61 persone che hanno subito gravi traumi (violenze, incidenti, lutti inattesi, ecc.). Il questionario utilizzato comprende il Post-Traumatic Growth Inventory (PTGI) tradotto e adattato in italiano. I punteggi di crescita post-traumatica risultano medio-bassi e appaiono legati al genere, al tempo passato dall’evento, alle emozioni positive e ad alcune forme di sostegno sociale e di strategie di coping. Parole Chiave: crescita post-traumatica; trauma; psicologia positiva; violenza, incidenti. Summary Post-traumatic growth: an opportunity after the trauma? There is considerable evidence in literature on the psychopathologic effects in trauma victims. However, post-trauma reactions also include the possibility of achieving growth in personal and interpersonal terms. This study aims to investigate the existence, extent and the relationships with other variables including social support, coping activities and emotions experienced after the trauma. The sample consists of 61 persons who have suffered severe trauma (violence, accidents, unexpected deaths, etc.). The questionnaire used includes the Post-Traumatic Growth Inventory (PTGI) adapted and translated into Italian. The results obtained indicate that the post-traumatic growth scores are medium-low and appear to be associated with the gender, the time elapsed from the event, the positive emotions and to some forms of social support and coping strategies.
Inter-active learning of ad-hoc classifiers for video visual analytics

Learning of classifiers to be used as filters within the analytical reasoning process leads to new and aggravates existing challenges. Such classifiers are typically trained ad-hoc, with tight time constraints that affect the amount and the quality of annotation data and, thus, also the users' trust in the classifier trained. We approach the challenges of ad-hoc training by inter-active learning, which extends active learning by integrating human experts' background knowledge to greater extent. In contrast to active learning, not only does inter-active learning include the users' expertise by posing queries of data instances for labeling, but it also supports the users in comprehending the classifier model by visualization. Besides the annotation of manually or automatically selected data instances, users are empowered to directly adjust complex classifier models. Therefore, our model visualization facilitates the detection and correction of inconsistencies between the classifier model trained by examples and the user's mental model of the class definition. Visual feedback of the training process helps the users assess the performance of the classifier and, thus, build up trust in the filter created. We demonstrate the capabilities of inter-active learning in the domain of video visual analytics and compare its performance with the results of random sampling and uncertainty sampling of training sets.
Synthesis of Property Monitors for Online Fault Detection

An original method for generating components that capture the occurrence of events is proposed, and logical and temporal properties of hardware/software embedded systems are monitored. The properties are written in PSL, under the form of assertions in declarative form. The method includes the construction of a library of primitive digital components for the PSL temporal and sequence operators. These building blocks are interconnected to construct complex properties, resulting in a synthesizable digital module that can be properly linked to the digital system under scrutiny.
The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection

This paper strives for video event detection using a representation learned from deep convolutional neural networks. Different from the leading approaches, who all learn from the 1,000 classes defined in the ImageNet Large Scale Visual Recognition Challenge, we investigate how to leverage the complete ImageNet hierarchy for pre-training deep networks. To deal with the problems of over-specific classes and classes with few images, we introduce a bottom-up and top-down approach for reorganization of the ImageNet hierarchy based on all its 21,814 classes and more than 14 million images. Experiments on the TRECVID Multimedia Event Detection 2013 and 2015 datasets show that video representations derived from the layers of a deep neural network pre-trained with our reorganized hierarchy i) improves over standard pre-training, ii) is complementary among different reorganizations, iii) maintains the benefits of fusion with other modalities, and v) leads to state-of-the-art event detection results. The reorganized hierarchies and their derived Caffe models are publicly available at http://tinyurl.com/imagenetshuffle.
Suzaku Spectroscopy of Extended X-Ray Emission in M17

We present the results of a Suzaku spectroscopic study of soft extended X-ray emission in the H II region of M 17. The spectrum of the extended emission was obtained with a high signal-to-noise ratio in a spatially resolved manner using the X-ray Imaging Spectrometer (XIS). We established that contamination by unresolved point sources, the Galactic Ridge X-ray emission, the cosmic X-ray background, and the local hot bubble emission is negligible in the background-subtracted XIS spectrum of the diffuse emission. Half a dozen emission lines were clearly resolved for the first time, including K lines of highly ionized O, Ne, and Mg as well as L-series complex of Fe at 0.5– 1.5 keV. Based on diagnosis of these lines, we obtained the following results: (1) the extended emission is an optically thin thermal plasma, represented well by a single temperature of 3.0 0.4 MK, (2) the abundances of elements with emission lines in the diffuse spectrum are 0.1–0.3 solar, while those of bright discrete sources are 0.3–1.5 solar, (3) the metal abundances relative to each other in the diffuse emission are consistent with solar, except for a Ne enhancement of a factor of 2, (4) both the plasma temperature and the chemical composition of the diffuse emission show no spatial variation across the studied spatial scale of 5 pc.
Psychometric properties of the Persian version of Sleep Hygiene Index in women

Background : Inappropriate sleep hygiene may result in disturbed daily functioning; therefore, the importance of using a sleep hygiene measurement scale is on the rise. The purpose of this study was to assess the reliability and validity of the Persian version of Sleep Hygiene Index (SHI) among the women of Kermanshah city. Method: In this study, 455 women were randomly selected. Data were collected from the study samples by SHI, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index. Twenty percent of participants recompleted the Sleep Hygiene Index after 4-6 weeks interval to measure test-retest reliability. Then, Pearson product-moment correlation coefficient between SHI and PSQI, ESS and ISI was computed. The factor structure of the SHI was evaluated by explanatory factor analysis. Results: The internal consistency of the questionnaire was 0.83 (P<0.01) and Sleep Hygiene Index was found to have a good test-retest reliability (r= 0.84, P<0.01). Explanatory factor analysis indicated three factors, `sleep-wake cycle behaviors` (four items), `bedroom factors` (three items), and ‘behaviors affecting sleep`(six items). Moreover, SHI was positively correlated with the total score of Pittsburgh Sleep Quality Index (r=0.61, P<0.01), Epworth Sleepiness Scale (r=0.63, P<0.01) and Insomnia Severity Index (r=0.62, P<0.01). Conclusion: The Persian version of SHI could be considered as a reliable and valid tool for evaluating sleep hygiene in Iranian women.
Robust Distributed Sensor Network Localization Based on Analysis of Flip Ambiguities

A major problem in wireless sensor network localization is erroneous local geometric realizations in some parts of the network due to the sensitivity to certain distance measurement errors, which may in turn affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In a recent study by the coauthors, an initial formal geometric analysis of flip ambiguity problems has been provided. The ultimate aim of that study was to quantify the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. In this paper we propose a more general robustness criterion to detect flip ambiguities in arbitrary sensor neighborhood geometries in planar sensor networks. This criterion enhances the recent study by the coauthors by removing the assumptions of accurately knowing some inter- sensor distances. The established robustness criterion is found to be useful in two aspects: (a) Analyzing the effects of flip ambiguity and (b) Enhancing the reliability of the location estimates of the prevailing localization algorithms by incorporating this robustness criterion to eliminate neighborhoods with flip ambiguity from being included in the localization process.