Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating health promotion in complex adaptive school systems: The Healthy Primary School of the Future

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Women's Euro 2005 a ‘watershed’ for women's football in England and a new era for the game?

ABSTRACT The UEFA Women's Euro 2005 Championship hosted in England can be seen as a significant event in the development of football for women, being the largest female, international tournament held in the UK up to that date. However, despite record crowds and unprecedented media attention, an increase in participation by women after the event was neither significant, nor sustained in the hosting region or nationally. As identified in Parliamentary committee evidence, grass roots women's football has continued to struggle, despite strategies investing heavily development and the ‘Superleague' to drive increased engagement in the game after 2005. Success in international events in 2012, 2015, and 2017 by England national teams showed spikes in media interest, audiences and progression on the field of play. However, an examination of national participation statistics demonstrates the difficulties of evidencing sport participation event legacy. The event in 2005 clearly contributed to changing public perceptions, stimulating a new strategic focus on women by the Football Association and therefore began the new era for the game, particularly in England. This research may provide some insight for planning for Euro 2021 – when football ‘comes home’ once again.
RESEARCH ON SPOKEN LANGUAGE PROCESSING

Despite the success of cochlear implantation in many deaf and hearingimpaired children, there remains considerable variability in individual language outcomes. Research suggests that implicit learning abilities might explain some of this variability. For example, Conway, Pisoni, Anaya, Karpicke, & Henning (under revision) found that implicit visual sequence learning abilities in normal hearing (NH) children and deaf children with cochlear implants (CIs) were related to individual differences in spoken language processing and development. Deaf children with CIs also had atypical sequence-learning abilities when compared to their NH peers, suggesting that in addition to normal language development, other cognitive functions may also be negatively affected by auditory deprivation. The current study investigated visual sequence learning as a possible early predictor of vocabulary development in NH and deaf infants. Two groups of NH infantsten 8.5-months-olds and six 5-month-oldsas well as two cochlear implanted infantsa 23and 30-month-oldwere presented with a three-location spatiotemporal sequence of multi-colored geometric shapes. Early language skills were assessed using the MacArthur-Bates CDI. Analyses of children’s reaction times to the stimuli suggest that children learned the spatiotemporal sequence and their increased reaction times were significantly correlated with vocabulary comprehension abilities. Clinical and theoretical implications are discussed.
Innovaciones introducidas en las empresas: identificación y comprensión

: e objective of this research is to identify if the products, processes, organizational and marketing practices introduced or applied are innovations. Companies from four sectors were analyzed; through content analysis, ﬁrms were classiﬁed into four groups according to their answers to the questions about whether they innovated and what type of innovation was introduced: 1) Companies that reported innovations and actually did; 2) companies that reported innovations but really did not; 3) companies that reported no innovations and actually did not; and 4)companies that reported no innovations but really did. e results show that organizational innovation is the least consolidated, as only half of the companies that initially responded to innovate in this area, theyreally did. In addition, the segment of small businesses has the lowest error rate; approximately six in 10 ﬁrms reported
Les aspects spatiaux et environnementaux de l'agriculture biologique

La protection de l’environnement (pris dans toutes ses composantes physiques, chimiques, biologiques, humaines) est l’un des objectifs majeurs mis en avant par les defenseurs de l’agriculture biologique (AB), au meme titre que la qualite des aliments, la sante des exploitants ou encore la rentabilite economique de l’exploitation. En ce sens, l’AB est souvent qualifiee d’agriculture durable et apparait comme un mode de production a promouvoir.
Biology and emotion: Optimal foraging and the partial reinforcement effect: a model for the teleonomy of feelings?

Preface Acknowledgements 1. Emotion since Darwin 2. Releasers and state-dependent reflexes 3. Purpose and emotion 4. Expression: a window on the emotions? 5. Are physiological changes epiphenomena of emotions? 6. Somatic influences on the emotion? 7. Optimal foraging and the partial reinforcement effect: a model for the teleonomy of feelings 8. Do emotions mature or differentiate? 9. Cognition, learning and emotion 10. Interaction of the components of emotion 11. Of mice and men 12. Biology and emotion: some conclusions Glossary Notes References Index.
The Development, Implementation, and Evaluation of a Campus-Based Culinary Nutrition Program for College Students

OF DISSERTATION THE DEVELOPMENT, IMPLEMENTATION, AND EVALUATION OF A CAMPUSBASED CULINARY NUTRITION PROGRAM FOR COLLEGE STUDENTS College students, on average, do not consume enough fruits and vegetables. Contributing to poor eating habits is an overall decline in young adults’ cooking skills as compared to previous decades, with today’s college students often relying on ubiquitous convenience food options. The detriments associated with these food choices are linked to a number of chronic diseases, including obesity. Though programming for college students which incorporates both nutrition education and hands-on cooking opportunities is rare, programs which have been implemented have had positive outcomes associated with increased self-efficacy with cooking and eating healthfully, and improved eating behaviors. This research utilized a mixed methods approach driven by the social cognitive theory to develop, implement, and assess the impact of a culinary nutrition education program, The College CHEF. The first phase of the research included conducting a PRECEDEPROCEED model-driven primary and secondary needs assessment to develop programming. The second phase consisted of program implementation and evaluation. The program was evaluated through a Qualtrics survey to determine participants’ changes preto –postwith cooking and eating attitudes, behaviors, self-efficacy and knowledge. Preand –postmeasures consisted primarily of Likert-type scales, in addition to demographic questions. Research participants were students living on University of Kentucky’s campus who were part of particular Living Learning Programs (LLPs), through which they lived, socialized, and often took classes together. Participants from two intervention groups (N = 15) attended four weekly 2-hour sessions, completing the measures online before and after the program. An inclusion criteria to be included in the study was that participants must have attended at least three of the four sessions. Control group participants (N = 17) did not partake in programming, but completed both preand post-surveys at the same time as the intervention groups. The study's results indicated that campus-based, hands-on culinary nutrition education programming was impactful in improving college students': 1) self-efficacy for using fruits, vegetables, and seasonings (p = .015); 2) fruit and vegetable consumption (p = 0.03); and 3) knowledge of cooking terms and techniques (p = .000). Given the limited research studying the impact of culinary nutrition education programming on college students, especially as it applies to those living in the same environment and reciprocally influencing one another, this study provides a unique perspective to the field of health promotion. Its findings can support campus-based, culinary nutrition programming for the college population in an effort to improve eating and cooking attitudes, behaviors, self-efficacy, and knowledge, and subsequently, overall health.
Preferential and random attachment models of a complex network

Analytic and numerical results are presented for three models of a complex network exhibiting growth via attachment. The first uses pure preferential attachment (the Barabási-Albert model), the second uses pure random attachment, and the third uses a mixture of both preferential and random attachment. System sizes of 10n for n = 1 to 6 were simulated and results found to be consistent with the analytic asymptotic degree distributions, with cut-offs occurring due to finite system size. The shape of the cut-offs was found to depend on the networks’ initial conditions. Numerically, the largest degree in a finite Barabási-Albert network was found to scale with system size like a power law over 5 orders of magnitude. Linear regression on a log-log plot gave an exponent of 0.51 ± 0.01, which is in good agreement with the theoretical prediction of asymptotic power law behaviour with exponent 12 .
Residual and Dense Connection Combine Fully Convolutional Network for Infant Brain MRI Segmentation

In the development of medical image segmentation, the application of convolutional neural networks has begun a profound revolution. The deep learning model is famous for excellent flexibility, efficiency and accuracy. The U-Net model is the beginning of task in the segmentation of medical images, which includes the basic operations of convolution, maxpooling, deconvolution, and concatenation. However, the U-Net model is disable to perform well on many types of data sets, because the model can’t solve the exact segmentation of the details. We proposed Residual and Dense Fully Convolutional Network (RDFCN) that consist of Residual Connection Block and Dense Connection Block, which makes up for the shortcomings of U-Net. The dataset we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu). This dataset is comprised of infant(between 6 and 9 months of age) brain MR images. After the testing, our model outperforms the U-Net and some of its improved models in evaluation of WM, GM and CSF.
Fluent Coordination in Proximate Human Robot Teaming

—Fluent coordination is important in order for teams to work well together. In proximate teaming scenarios, fluent teams tend to perform more successfully. Recent work suggests robots can support fluency in human-robot teams a number of ways, including using nonverbal cues and anticipating human intention. However, this area of research is still in its early stages. We identify some of the key challenges in this research space, specifically individual variations during teaming, knowledge and task transfer, co-training prior to task execution, and long-term interactions. We then discuss possible paths forward, including leveraging human adaptability, to promote more fluent teaming.
Occupant tracking using model-based data interpretation of structural vibrations

Sensor-based occupant tracking has potential to increase understanding of building utilization. Most occupant-localization methodologies involve costly equipment that needs regular maintenance and relies on intrusive sensing methods such as radio-frequency and optical sensor approaches. Structural-vibration-based methodologies often employ model-free techniques to triangulate the location of an occupant and thus, might lead to ambiguous interpretation in presence of obstructions in the structure. Through sparsely distributed sensors in floor slabs, incorporating finite-element simulations of various trajectories into data interpretation has the potential to provide better accuracy for occupant tracking on floors with varying rigidity. This paper presents an application of model-based data interpretation, to track an occupant within an office environment using footstep-induced vibration time histories. Error-domain model falsification, which is used to compare measured signals with simulation results, is a multiplemodel approach that explicitly accommodates ambiguous data interpretation. It provides a set of candidate trajectories from an initial population of possible trajectories through incorporating various sources of uncertainties. It is shown that error-domain model falsification reduces the uncertainty of occupant tracking by falsifying wrong trajectories and this has potential to increase tracking performance for accurate occupant localization in smart building applications.
Vascular Complications in Diabetes Mellitus

Vascular complications are pivotal to the devastating effects of diabetes mellitus, and occur as a result of hyperglycemia engendered link between the disease and oxidative stress. In the diabetics, vascular tissues are damaged due to hyperglycemia-mediated free radicals, particularly through oxidation of essential biomoecules such as DNA, proteins and lipids in these tissues. Depending on the type of blood vessels affected, there are different categories of diabetic complications. The two major ones are micro vascular and macro vascular complications. The latter indicates damage to large blood vessels, while the former describes damage to small blood vessels. This mini-review provides some important information on vascular complications in the diabetics.
Single Image Highlight Removal for Real-Time Image Processing Pipelines

This paper presents a fully automatic method for the separation of diffuse and specular reflection components from a single image. Overall, the mechanisms in which the available methods operate on are computationally costly and do not translate well to modern hardware-implemented image processing pipelines, such as the ones present in consumer electronics. Consequently, the objective of this article is to introduce a simple yet effective method for specular highlight removal. It is based on the dichromatic reflection model and operates through histogram matching in the YCbCr color space. The proposed method performs in real-time. It only uses global image statistics and point-wise intensity transformations. Experimental evaluation shows that the proposed approach has competitive results in comparison to state-of-the-art methods. Limitations of the proposed approach are seldom and are common to most methods available. The proposed method, however, achieves better quality results with much less computational cost, thus enabling feasibility in systems with limited processing power.
Flexible and Soft Inductive Tri-axis Tactile Sensor Using Liquid Metal as Sensing Target

Inductive sensing is a promising approach for flexible and soft tactile sensors; however, magnetic materials used in previously proposed inductive tactile sensors have a trade-off between sensitivity and softness. This paper proposes a flexible and soft inductive tri-axis tactile sensor using liquid metal as the sensing target to overcome this trade-off. We investigated the sensor responses and also compared them with a sensor fabricated using a magnetorheological elastomer (MRE). We demonstrated that a small pool of liquid metal covered with silicone rubber is an effective material for soft inductive sensors. The applied tri-axis force can be obtained through calibration. The results showed that the signal-to-noise ratio was approximately 1.7 times higher and the sensitivities in terms of the shear and normal forces were approximately 6.6 and 4.2 times higher than that observed in the MRE-based inductive sensor, respectively. The proposed sensor offers both high sensitivity and a soft and stretchable surface.
Adversarial Training Methods for Boltzmann Machines

A Restricted Boltzmann Machines (RBM) is a generative Neural Net that is typically trained to minimize <italic>KL</italic> divergence between data distribution <inline-formula> <tex-math notation="LaTeX">${P} _{data}$ </tex-math></inline-formula> and its model distribution <inline-formula> <tex-math notation="LaTeX">${P} _{RBM}$ </tex-math></inline-formula>. However, minimizing this <italic>KL</italic> divergence does not sufficiently penalize an RBM that place a high probability in regions where the data distribution has a low density, and therefore, RBMs always generate blurry images. In order to solve this problem, this paper extends the loss function of RBMs from <italic>KL</italic> divergence to adversarial loss and proposes an Adversarial Restricted Boltzmann Machine (ARBM) and an Adversarial Deep Boltzmann Machine (ADBM). Different from the other RBMs, an ARBM minimizes its adversarial loss between the data distribution and its model distribution without explicit gradients. Different from traditional DBMs, an ADBM minimizes its adversarial loss without a layer-by-layer pre-training. In order to generate high-quality color images, this paper proposes an Adversarial Hybrid Deep Generative Net (AHDGN) based on an ADBM. The experiments verify that the adversarial loss can be minimized in our proposed models, and the generated images are comparable with the current state-of-the-art results.
Biofeedback Methodology: a Narrative Review

In this paper, we review the state of biofeedback methodology 50 years after its inception, with the objective to facilitate well-informed, scientific discourses around the topic. The scope of our review is limited to quantitative analysis of body of work published over the last decade, from which it is evident that biofeedback experiences a major methodological shift - away from strictly confined medical and research facilities, and towards personal, consumer-level use, thanks to wireless gadgets, wearables and smart devices available en masse. With such change come new opportunities and challenges, in the form of technological aspects, such as data processing trade-offs, efficiency of sensor placement, modalities and architectures, as well as general effectiveness of biofeedback in novel application areas, surrounded by privacy concerns and implications of e-health mobile technologies, which already available, but not yet ready for the prime-time. It is our intent to reflect on this liminal state of affairs.
An Improved Colpitts VCO With Low Phase Noise Using a GaAs BiHEMT Process

In this letter, an improved balanced Colpitts voltage-controlled oscillator (VCO) with a collector–emitter cross-coupled capacitors is presented. The proposed Colpitts structure can introduce a more robust oscillation startup condition. The noise-shifting technique is used to enhance the loaded quality factor of the tank, which improves the phase noise in turn. In addition, three-bit switches composed of enhancement-mode high-electron mobility transistors (E-HEMTs) are utilized to expand the tuning range and reconfigurability of the proposed VCO. In proof of the concept, a VCO prototype is fabricated in a GaAs BiHEMT [enhancement- and depletion-mode high-electron mobility transistor (E/D-PHEMT)] process. The measured results demonstrate the best phase noise of −139.46 dBc/Hz at 1-MHz offset. To the best of our knowledge, this is the lowest phase noise among the reported ones fabricated on-chip with a similar frequency band, and it achieves a wide tuning frequency bandwidth of 21% (2.65–3.27 GHz) with peak figure of merit (FoM) of −192.26 dB and FOMT of −198.8 dB.
Enterprise Knowledge Modeling, UML vs Ontology: Formal Evaluation

Everyday activities in enterprises rely heavily on the experts’ know-how. Due to experts departure, the loss of expertise and knowledge is a reoccurring problem in these enterprises. Recently, in order to capture experts knowledge into intelligent systems, formal knowledge representation methods, such as ontologies, are being studied and have caught up with non-formal or semi-formal representation, such as UML. The similarities and differences between UML class diagram and computational ontology have for long raised questions about the possibility of synthesizing them in a common representation (usually an ontology). Indeed, the problem of migrating knowledge encoded in UML into an ontology is an active research domain. This paper outlines our approach, which is based on semantic matching between existing ontologies and a UML class diagram, to support UML driven ontology refactoring and engineering.
Research on Adaptive Iterative Learning Control of Air Pressure in Railway Tunnel With IOTs Data

When a train enters a tunnel, the passengers in the train will feel tinnitus. The main reason is that the pressure in the tunnel enters the vehicle through the adjusting system of the train, which will cause discomfort to the passengers. In this paper, according to the quasi-periodicity and repeatability of mass data in the process of train running in tunnels, a control method based on the IOTs big data is proposed, and an adaptive iterative learning control algorithm based on the IOTs big data is established. The fan operation frequency of ventilation system is regulated by adaptive iterative learning control algorithm, and can adjust the new air and exhaust gas of the ventilation system in real time to restrain the pressure fluctuation in the train. Finally, the simulation results show that the adaptive iterative learning control algorithm based on the Internet of Things can significantly reduce the amplitude of pressure fluctuation in the tunnel and the change rate of the ventilator, and improve the passenger comfort program. Moreover, the real-time measured data also show that the proposed closed-loop adaptive iterative learning control algorithm based on the Internet of Things is obviously superior to active control.
A Novel Low Power and Reduced Transistor Count Magnetic Arithmetic Logic Unit Using Hybrid STT-MTJ/CMOS Circuit

One of the major concern for CMOS technology is the increase in power dissipation as the technology node lowers down to deep submicron region. Magnetic tunnel junction (MTJ) working on Spin transfer torque (STT) switching mechanism is recognized as one of the most promising spintronic device for post CMOS era due to its non-volatility, high speed, high endurance, CMOS compatibility and mainly the low power dissipation which can offer the solutions for the problems posed by existing CMOS technology. We have proposed a novel logic-in-memory (LIM) architecture of magnetic arithmetic logic unit (P-MALU) based on hybrid STT-MTJ/CMOS circuits. Simulation results reveal that there is significant reduction in the total power dissipation and transistor count of arithmetic unit by 28.44% and 29.16% compared to double pass transistor logic based clocked CMOS ALU design (DPTL-C2MOS-ALU), while 58.87% and 45.16% to modified magnetic arithmetic logic unit (M-MALU) respectively. Reduction in average power dissipation for logical unit is 37.61% and 52.55% along with 47.22% and 42.42% fewer transistors than DPTL-C2MOS-ALU and M-MALU design respectively. Monte-Carlo(MC) simulation is then performed by incorporating process and mismatch variations for CMOS and extracted parameters of MTJ, to study the behavior of DPTL-C2MOS-ALU, M-MALU and P-MALU designs in terms of power dissipation. All the simulation results reveal that the P-MALU is superior than other two ALU designs in terms of power dissipation, delay and device count. Further, the P-MALU circuit is extended for 4-bits arithmetic operations. Electrical simulations are performed to verify the functionality of the design for higher bit operations which demonstrates the feasibility of the proposed design in VLSI circuits.