Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Resistance Training and Combined Training Program on Repeated Sprint Ability in Futsal Players

Abstract The purpose of this study was to compare the effects of 6 weeks resistance training (RT) with combined RT and loaded change of direction (CD) exercise on muscle strength and repeated sprint ability (RSA) in futsal players. Thirty-four players (age: 23.7±4.1 years; height: 1.77±0.06 m; body mass: 74.1±8.2 kg) were randomly assigned into three groups: full squat group (SG, n=12), combined full squat and CD group (S+CDG, n=12), and control group (CG, n=10). The RT for SG consisted of full squat with low-load (~45–60% 1RM) and low-volume (2-3 sets and 4-6 repetitions), whereas the S+CDG performed the same RT program combined with loaded CD (2-5 sets of 10 s). Estimated one-repetition maximum (1RMest) and variables derived from RSA test including mean sprint time (RSAmean), best sprint time (RSAbest), percent sprint decrement (Sdec), mean ground contact time (GCTmean) and mean step length (SL) were selected as testing variables. Changes in sprint time and GCT in each sprint were also analysed. Both experimental groups showed significant (P<0.05-0.001) improvements for 1RMest, RSAbest and first and second sprint time. In addition, S+CDG achieved significant (P<0.05-0.001) improvements in RSAmean, sprint time (from fifth to ninth sprint) and GCT (from third to eighth sprint). These results indicate that only 6 weeks of low-load and low-volume RT combined with CD in addition to routine futsal training is enough to improve RSA and strength performance simultaneously in futsal players.
Machine Learning Approaches along the Radiology Value Chain - Rethinking Value Propositions

Radiology is experiencing an increased interest in machine learning with its ability to use a large amount of available data. However, it remains unclear how and to what extent machine learning will affect radiology businesses. Conducting a systematic literature review and expert interviews, we compile the opportunities and challenges of machine learning along the radiology value chain to discuss their implications for the radiology business. Machine learning can improve diagnostic quality by reducing human errors, accurately analysing large amounts of data, quantifying reports, and integrating data. Hence, it strengthens radiology businesses seeking product or service leadership. Machine learning fosters efficiency by automating accompanying activities such as generating study protocols or reports, avoiding duplicate work due to low image quality, and supporting radiologists. These efficiency improvements advance the operational excellence strategy. By providing personnel and proactive medical solutions beyond the radiology silo, machine learning supports a customer intimacy strategy. However, the opportunities face challenges that are technical (i.e., lack of data, weak labelling, and generalisation), legal (i.e., regulatory approval and privacy laws), and persuasive (i.e., radiologists’ resistance and patients’ distrust). Our findings shed light on the strategic positioning of radiology businesses, contributing to academic discourse and practical decision-making.
Maize Yield Response to Nitrogen Rate and Plant Density under Film Mulching

Agronomy Journa l • Volume 110 , I s sue 3 • 2018 Water stress is the main factor limiting crop production in dryland areas of the world (Zand-Parsa et al., 2006; Li et al., 2009). On the Loess Plateau in northwest China, more than 80% of arable land is dominated by rain-fed cropping systems (Huang et al., 2011). Low crop production in this region is closely associated with limited and unevenly distributed precipitation, low water availability, and high evaporation (Zhou et al., 2009). This issue has become increasingly serious due to climate change, which significantly threatens agricultural sustainability in rain-fed arid and semiarid areas (Pan et al., 2011; Tao and Zhang 2011). Therefore, increasing water use efficiency (WUE) through highly effective utilization of precipitation is a key priority to improve agricultural production and maintain food security in the Loess Plateau region. The film mulching technique has proven to be an effective measure to improve grain yield and WUE in semiarid and arid farming areas (Jia et al., 2006; Ramakrishna et al., 2006; Zhao et al., 2012). Recently, a novel technique using double ridges and furrows mulched with plastic film was found to be more efficient for improving grain yield and WUE in spring maize compared with the conventional practices (Zhou et al., 2009; Ye and Liu, 2012; Liu et al., 2014a). This new planting pattern contributed to better soil water conditions and higher topsoil temperature, especially during seedling development; it increased seed fertility and in turn resulted in the final yield increase (Zhou et al., 2009). This technique has been widely applied in dryland farming systems, particularly in areas characterized by lack of irrigation and low spring temperatures (Zhang et al., 2005; Liu et al., 2009). However, irrational N application regimes (Liu et al., 2014b; Li et al., 2015) and low plant densities (Liu et al., 2014a) are the key factors limiting the further improvement of crop yield and resource use efficiency in this mulching system. N fertilizer plays a significant role in improving grain yield and WUE (Hernández et al., 2015; Li et al., 2015). Previous research focusing on contrasts in water availability (i.e., rain-fed Maize Yield Response to Nitrogen Rate and Plant Density under Film Mulching
A study of cardiovascular function under controlled and spontaneous ventilation in isoflurane-medetomidine anaesthetized horses.

OBJECTIVE To determine, in mildly hypercapnic horses under isoflurane-medetomidine balanced anaesthesia, whether there is a difference in cardiovascular function between spontaneous ventilation (SV) and intermittent positive pressure ventilation (IPPV). STUDY DESIGN Prospective randomized clinical study. ANIMALS Sixty horses, undergoing elective surgical procedures under general anaesthesia: ASA classification I or II. METHODS Horses were sedated with medetomidine and anaesthesia was induced with ketamine and diazepam. Anaesthesia was maintained with isoflurane and a constant rate infusion of medetomidine. Horses were assigned to either SV or IPPV for the duration of anaesthesia. Horses in group IPPV were maintained mildly hypercapnic (arterial partial pressure of carbon dioxide (PaCO(2)) 50-60 mmHg, 6.7-8 kPa). Mean arterial blood pressure (MAP) was maintained above 70 mmHg by an infusion of dobutamine administered to effect. Heart rate (HR), respiratory rate (f(R)), arterial blood pressure and inspiratory and expiratory gases were monitored continuously. A bolus of ketamine was administered when horses showed nystagmus. Cardiac output was measured using lithium dilution. Arterial blood-gas analysis was performed regularly. Recovery time was noted and recovery quality scored. RESULTS There were no differences between groups concerning age, weight, body position during anaesthesia and anaesthetic duration. Respiratory rate was significantly higher in group IPPV. Significantly more horses in group IPPV received supplemental ketamine. There were no other significant differences between groups. All horses recovered from anaesthesia without complications. CONCLUSIONS There was no difference in cardiovascular function in horses undergoing elective surgery during isoflurane-medetomidine anaesthesia with SV in comparison with IPPV, provided the horses are maintained slightly hypercapnic. CLINICAL RELEVANCE In horses with health status ASA I and II, cardiovascular function under general anaesthesia is equal with or without IPPV if the PaCO(2) is maintained at 50-60 mmHg.
Last Gasp of the Grenville Orogeny: Thermochronology of the Grenville Front Tectonic Zone near Killarney, Ontario

We present U-Pb (titanite, zircon) and $^{40}Ar/^{39}Ar$ (hornblende, mica, K-feldspar) data from a transect across the western part of the Grenville Front Tectonic Zone (GFTZ) near Killarney, Ontario. High-grade metamorphic assemblages (~1450 Ma) in this part of the GFTZ pre-date the Grenvillian orogeny and were primarily exhumed, with little or no metamorphic overprinting, by Grenvillian deformation. The titanite and zircon data form a discordant array with an upper intercept of $1454 \pm 8 Ma$ and a lower intercept of $978 \pm 13 Ma$. These data are interpreted in terms of partial lead loss during a short-lived thermal event that increased in intensity from west to east across the transect. $^{40}Ar/^{39}Ar$ data from hornblende indicate cooling through ~450°C at ~993-979 Ma, multiple diffusion domain models for the interpretation of discordant K-feldspar spectra indicate cooling through ~365-340°C at 990-960 Ma, and muscovite data indicate cooling through ~320°C at -930 Ma. Biotite data are not easily interpreted owing to the effects of partial resetting and/or excess $^{40}Ar$. The thermochronological data suggest that a thermal event with peak temperatures of 500-600°C affected the GFTZ at ~980 Ma, followed by very rapid cooling to ~350°C. We interpret the data in terms of a tectonic model involving rapid exhumation of GFTZ rocks (in response to erosion) in the hangingwall of a crustal-scale shear zone developed during a ~980 Ma episode of convergence.
Possession Sketches : Mapping NBA Strategies

We present Possession Sketches, a new machine learning method for organizing and exploring a database of basketball player-tracks. Our method organizes basketball possessions by offensive structure. We irst develop a model for populating a dictionary of short, repeated, and spatially registered actions. Each action corresponds to an interpretable type of player movement. We examine statistical patterns in these actions, and show how they can be used to describe individual player behavior. Leveraging this “vocabulary” of actions, we develop a hierarchical model that describes interactions between players. Our approach draws on the topic-modeling literature, extending Latent Dirichlet Allocation (LDA) through a novel representation of player movement data which uses techniques common in animation and video game design. We show that our model is able to group together possessions with similar offensive structure, allowing ef icient search and exploration of the entire database of playertracking data. We show that our model inds repeated offensive structure in teams (e.g. strategy), providing a much more sophisticated, yet interpretable lens into basketball player-tracking data.
Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level

The ability to model and automatically detect dialogue act is an important step toward understanding spontaneous speech and Instant Messages. However, it has been difficult to infer a dialogue act from a surface utterance because it highly depends on the context of the utterance and speaker linguistic knowledge; especially in Arabic dialects. This paper proposes a statistical dialogue analysis model to recognize utterance's dialogue acts using a multi-classes hierarchical structure. The model can automatically acquire probabilistic discourse knowledge from a dialogue corpus were collected and annotated manually from multi-genre Egyptian call-centers. Extensive experiments were conducted using Support Vector Machines classifier to evaluate the system performance. The results attained in the term of average F-measure scores of 0.912; showed that the proposed approach has moderately improved F-measure by approximately 20%.
Effective Method for Automatic Generation of Knowledge from Electronic Textbook

: With the tremendous increase in traffic and tools in virtually every organization we need such kind of tool that will perform all the operation regarding education system in effective way so that technology supported learning system should be helpful in many learning situations. we need to generate domain module and domain module contain the knowledge but generation of domain module require more cost and hence electronic textbook has been developed which is a core representation of the domain module. So, we use natural processing techniques, heuristic reasoning and ontology’s for semiautomatic construction of domain module from electronic textbook.
Care Interventions for Patients with Heart Failure : A Systematic Review and Meta-Analysis

Objective: To systematically characterize interventions and effectiveness of palliative care for advanced heart failure (HF) patients. Background: Patients with advanced heart failure experience a high burden of distressing symptoms and diminished quality of life. Palliative care expertise with symptom management and healthcare decision-making benefits HF patients. Methods: A systematic PubMed search was conducted from inception to June 2016 for studies of palliative care interventions for HF patients. Studies of humans with a HF diagnosis who underwent a palliative care intervention were included. Data were extracted on study design, participant characteristics, intervention components, and in three groups of outcomes: patient-centered outcomes, quality-of-death outcomes, and resource utilization. Study characteristics were examined to determine if meta-analysis was possible. Results: The fifteen identified studies varied in design (prospective, n = 10; retrospective, n = 5). Studies enrolled older patients, but greater variability was found for race, sex, and marital status. A majority of studies measuring patient-centered outcomes demonstrated improvements including quality of life and satisfaction. Quality-of-death outcomes were mixed with a majority of studies reporting clarification of care preferences, but less improvement in death at home and hospice enrollment. A meta-analysis in three studies found that homebased palliative care consults in HF patients lower the risk of rehospitalization by 42% (RR = 0.58; 95% Confidence Interval 0.44, 0.77). Discussion: Available evidence suggests that home and team-based palliative interventions for HF patients improve patient-centered outcomes, documentation of preferences, and utilization. Increased high quality studies will aid the determination of the most effective palliative care approaches for the HF population.
Robust algorithms for anatomic plane primitive detection in MR

One of primary challenges in the medical image data analysis is the ability to handle abnormal, irregular and/or partial cases. In this paper, we present two different robust algorithms towards the goal of automatic planar primitive detection in 3D volumes. The overall algorithm is a bottoms-up approach starting with the anatomic point primitives (or landmarks) detection. The robustness in computing the planar primitives is built in through both a novel consensus-based voting approach, and a random sampling-based weighted least squares regression method. Both these approaches remove inconsistent landmarks and outliers detected in the landmark detection step. Unlike earlier approaches focused towards a particular plane, the presented approach is generic and can be easily adapted to computing more complex primitives such as ROIs or surfaces. To demonstrate the robustness and accuracy of our approach, we present extensive results for automatic plane detection (Mig-Sagittal and Optical Triangle planes) in brain MR-images. In comparison to ground truth, our approach has marginal errors on about 90 patients. The algorithm also works really well under adverse conditions of arbitrary rotation and cropping of the 3D volume. In order to exhibit generalization of the approach, we also present preliminary results on intervertebrae-plane detection for 3D spine MR application.
An improved SMURF scheme for cleaning RFID data

RFID technology is widely used in the Internet of Things (IoT) environment for object tracking. With the expansion of its application areas, the demand for reliability of business data is increasingly important. In order to fulfil the needs of upper-level applications, data cleaning is essential and directly affects the correctness and completeness of the business data, so it needs to filter and handle RFID data. The traditional statistical smoothing for unreliable RFID data (SMURF) algorithm is only aimed at constant speed data flow during the process of data cleaning. In this paper, we overcome the shortage of SMURF algorithm, and an improved SMURF scheme in two aspects is proposed. The first one is based on dynamic tags, and the second one considers the influence of data redundancy. The experiments verify that the improved scheme is reasonable in dynamic settings of sliding window, and the accuracy is improved as well.
A Novel and Inexpensive Approach for Force Sensing Based on FSR Piezocapacitance Aimed at Hysteresis Error Reduction

Force-sensing resistors (FSRs) are inexpensive alternatives to load cells. They are suitable for applications where noninvasive devices are needed to measure force, stress, or pressure. However, they have been proved to be hysteresis prone and offer nonrepeatable readings due to their highly voltage-dependent electrical resistance. A piezocapacitive effect has been found as an alternative phenomenon that is able to offer force-dependent readings of capacitance with less hysteresis error. Also, this capacitance is not dependent on voltage, which also improves repeatability in force measurements. Since measuring capacitance is more expensive than resistance, the least costly conditioning circuitry is desired. An inexpensive alternative using an LM555 that oscillates depending on capacitance is here presented. Hysteresis and repeatability errors have been reduced for a widespread-used force-sensing resistor brand.
Broad Learning:: An Emerging Area in Social Network Analysis

Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide users with various types of services. Generally, information available in these online social networks is of diverse categories, which can be represented as heterogeneous social networks (HSNs) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously, who can act as the anchors aligning different social networks together. So multiple HSNs not only represent information in each social network, but also fuse information from multiple networks. Formally, the online social networks sharing common users are named as the aligned social networks, and these shared users are called the anchor users. The heterogeneous information generated by users' social activities in the multiple aligned social networks provides social network practitioners and researchers with the opportunities to study individual user's social behaviors across multiple social platforms simultaneously. This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks, including network alignment, link prediction, community detection, information diffusion and network embedding respectively.
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense

Multi-sense word embedding is an important extension of neural word embeddings. By leveraging context of each word instance, multi-prototype version of word embeddings were accomplished to represent the multi-senses. Unfortunately, this kind of context based approach inevitably produces multiple senses which should actually be a single one, suffering from the various context of a word. (Shi et al., 2016) used WordNet to evaluate the neighborhood similarity of each sense pair to detect such pseudo multi-senses. In this paper, a novel framework for unsupervised corpus sense tagging is presented, which mainly contains four steps: (a) train multi-sense word embeddings on the given corpus, using existing multi-sense word embedding frameworks; (b) detect pseudo multi-senses in the obtained embeddings, without requirement to any extra language resources; (c) label each word in the corpus with a speciﬁc sense tag, with respect to the result of pseudo multi-sense detection; (d) re-train multi-sense word embeddings with the pre-selected sense tags. We evaluate our framework by training word embeddings with the obtained sense speciﬁc corpus. On the tasks of word similarity, word analogy as well as sentence understanding, the embeddings trained on sense-speciﬁc corpus obtain better results than the basic strategy which is applied in step (a).
Insomnia and eating expectancies among college students: the role of emotion dysregulation

Abstract There is limited understanding of the relation between insomnia and aspects of eating among college students though available data suggest salient clinical relationships. The present study explored a potential transdiagnostic mechanism in the relation between insomnia symptoms and eating expectancies. Participants were a racially/ethnically diverse sample of 1589 college students (80.4% females; Mage = 22.2 years, SD = 5.27) from an urban university. Primary analysis included three regression-based models of eating expectancies. Insomnia symptoms served as the predictor, and emotion dysregulation served as the indirect (mediator) variable in all models. Results indicated that insomnia symptoms yielded a significant indirect effect through emotion dysregulation on expectancies of eating to help manage negative affect, alleviate boredom, and lead to feeling out of control. Students experiencing insomnia symptoms may be at higher risk for experiencing dysregulated emotions and consequently maladaptive eating expectancies compared to good sleepers.
Annotating Reflections for Health Behavior Change Therapy

We present our work on annotating reflections, an essential counselor behavioral code in motivational interviewing for psychotherapy on conversations that are a combination of casual and therapeutic dialogue. We annotated all the therapists’ utterances from ten transcripts spanning more than five hours of conversation with Complex Reflection, Simple Reflection, or No Reflection. We also provide insights into corpus quality and code distributions. The corpus that we constructed and annotated in this effort is a vital resource for automated health behavior change therapy via a dialogue system. As the on-going work, additional conversations are being annotated at least by one annotator.
Creating Water Sensitive Cities in Australia

DOW Chemicals (Australia) Limited joined with the International WaterCentre, which is dedicated to developing leadership capacity in sustainable and integrated water practices in Australia and internationally. The cocept of 'Water Sensitive Cities' came into affect in 2004 when it was adopted by the Australian Government under the National Water Initiative.
Sentiment Classification with Convolutional Neural Network using Multiple Word Representations

Most neural network models for sentiment classification use word vectors pre-trained by word embedding methods to represent a word. Although word vectors are trained on large corpus, most of them are restricted by the vocabularies in the corpus. Since sentiment classification models have to capture subtle meaning of sentence, it is desirable to represent words that have not been pre-trained by word embedding method. To achieve this goal, we propose a sentiment classification model with convolutional neural network using multiple word representations. Werepresent a word by three embedding methods including word2vec, GloVe, and our method which is based on a character level embedding method that successfully captures subtle differences between words. Experimental results from three datasets show that our model with an additional character level embedding method improves the accuracy of the sentiment classification.