Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination of the reliability of Gait Assessment and Intervention Tool in patients with a stroke

The Gait Assessment and Intervention Tool (GAIT) has been recently developed for gait assessment in patients with stroke, and it is considered as a tool that may identify clinically relevant deviations from normal gait and quantifies relevant changes in gait because of an intervention. The objective was to analyze the GAIT psychometric properties in participants with stroke. A convenience sample of 15 adults with stroke was selected (58.15±7.8 years; 5.38±3.3 years from injury). We used the GAIT for evaluating the gait in patients with stroke. GAIT scoring is based on observations of anterior/posterior and lateral-view video documents. Inter-rater reliability of the GAIT between trained raters was good (intraclass correlation coefficient=0.762; P=0.008; 95% confidence interval=0.749–0.926). The minimal detectable change was 7.68 points (12.39%). Inter-rater reliability for the hip, knee, and ankle items was also good. In conclusion, the current study demonstrated that GAIT has good inter-rater reliability and good internal consistency.
Consuming Almonds vs. Isoenergetic Baked Food Does Not Differentially Influence Postprandial Appetite or Neural Reward Responses to Visual Food Stimuli

Nuts have high energy and fat contents, but nut intake does not promote weight gain or obesity, which may be partially explained by their proposed high satiety value. The primary aim of this study was to assess the effects of consuming almonds versus a baked food on postprandial appetite and neural responses to visual food stimuli. Twenty-two adults (19 women and 3 men) with a BMI between 25 and 40 kg/m2 completed the current study during a 12-week behavioral weight loss intervention. Participants consumed either 28 g of whole, lightly salted roasted almonds or a serving of a baked food with equivalent energy and macronutrient contents in random order on two testing days prior to and at the end of the intervention. Pre- and postprandial appetite ratings and functional magnetic resonance imaging scans were completed on all four testing days. Postprandial hunger, desire to eat, fullness, and neural responses to visual food stimuli were not different following consumption of almonds and the baked food, nor were they influenced by weight loss. These results support energy and macronutrient contents as principal determinants of postprandial appetite and do not support a unique satiety effect of almonds independent of these variables.
Stability Assessment and Optimization Methods for Microgrid With Multiple VSG Units

The virtual synchronous generator (VSG) is a control scheme for inverter-based generating units that uses a synchronous generator (SG) model to emulate the dynamic behavior of a SG in order to enhance the stability of the system. In this paper, the multi-VSG microgrid is introduced and the voltage angle deviations (VADs) of generators with respect to the angle of the center of inertia are defined as a tool for transient stability assessment of the multi-VSG microgrid. Afterward, particle swarm optimization is implemented to tune the parameters of the VSG units in order to achieve two objectives: first, to have a smooth transition after a change or disturbance and second, to maintain the VADs of generators within a specific limit. Moreover, another scheme termed alternating inertia is applied to the VSGs of the microgrid to suppress the oscillation quickly and improve transient stability after a large disturbance. This scheme switches the value of the moment of inertia of VSGs considering the angular frequency of the VSG with respect to the equilibrium point and its rate of change. The introduced multi-VSG microgrid is simulated by PSCAD/EMTDC and the performance of the proposed methods is evaluated.
The Polyethylene Casing as Diffusion Barrier for Polyurethane Insulated District Heating Pipes

Most district heating pipes are insulated with polyurethane foam in order to minimise heat losses. A high-density polyethylene (HDPE) casing protects the insulated pipe, and its permeability properties for the polyurethane cell gases, including air, play an important role for the long-term insulating capacity. The permeability of the HDPE casing of a district heating pipe was studied. Two methods were used to determine the permeability: I) by measuring the mass transfer through a sample, and II) by measuring the sorption and desorption of the gas in a sample. The experimental procedures are described. For carbon dioxide a permeability coefficient of about 9·10-16 mole·m-1·s-1·Pa-1 was found by both methods. For oxygen and nitrogen only method I was used and the permeability coefficients found were 1.9 and 0.6·10-16 mole·m-1·s-1·Pa-1, respectively.
Analysis of Frictional Noise for Water Lubricated Rubber Bearings System

A finite element model of a water lubricated rubber bearings system is established by using ABAQUS software. A complex eigenvalue analysis of the bearings system is performed. The occurrence propensity of noise is evaluated on the basis of positive and negative of the real parts of eigenvalues. If there are one or more positive real parts, the system is considered to have the propensity of noise occurrence. In the ABAQUS approach, the direct contact coupling at the shaft/bearing interface is applied to obtaining normal contact forces and there is no need to introduce contact springs at the interface. The approach is easily used to establish a non-flat sliding contact model for noise. By using the ABAQUS finite element model, effects of the friction coefficient, the properties of rubber material and the thickness of rubber are studied. The results show that the friction coefficient, the properties of rubber material and the thickness of rubber have distinct effects on the occurrence of noise.
Efficiency of Lasers and a Desensitizer Agent on Dentin Hypersensitivity Treatment: A Clinical Study

Aim: The aim of this clinical study was to determine and compare the efficiency of the glutaraldehyde-containing agent (GCA), Nd:YAG, Er,Cr:YSGG lasers, and the combination of them on the dentin hypersensitivity (DH) treatment. Subjects and Methods: This study was performed with the participation of 17 healthy adult patients having 100 teeth with DH; the patients were randomly divided into five groups according to the treatment protocol: (1) application of GCA on sensitive teeth, (2) Nd:YAG laser (1 W/cm2, 10 Hz) irradiation on sensitive teeth, (3) application of GCA on sensitive teeth and then Nd:YAG laser irradiation, (4) Er,Cr:YSGG laser (0.25 W/cm2, 20 Hz) irradiation on sensitive teeth, (5) application of GCA on sensitive teeth and then Er,Cr:YSGG laser irradiation. Sensitivity levels were assessed by the Yeaple probe on the buccal surfaces of the teeth at a force setting of 10 g. Measurements were performed for 30 min, after 7, 90, and 180 days of the therapy to assess the effects of desensitization. The evaluations were analyzed using the one-way analysis of variance and repeated measurement test (P < 0.05). Results: After sessions, DH was significantly reduced in all groups at each measurement point. The Er,Cr:YSGG laser with or without GCA application were the most effective ones in DH treatment (P < 0.05). Comparison of the treatment regimens demonstrated that the scores achieved with the Yeaple probe were not significantly higher for the Nd:YAG laser groups than the GCA alone group. Conclusions: This clinical study shows that the Er,Cr:YSGG laser have promising potential for the treatment of DH.
Inclusive Learner Model for Adaptive Recommendations in Virtual Education

Since last two decades virtual education has brought with it big challenges for higher education, which suppose having in mind the feasibility of delivering digital scenarios that adjusts to learner's characteristics. Thus, our first concern involves distinguishing heterogeneity of student's characteristics who can access this modal of education. Other issue to have in mind is to provide a Learning Management Systems (LMS) that can be adapted to student's characteristics from that diversity. This paper focuses on presenting a learner model with regards to educational special needs, in order to achieve some adaptation through an LMS. The model was built based on a particular data set identified in previous studies done, that include: demographics, competences, reading profile, learning styles, and cognitive traits [1]. The main objective of this model is to bring adaptive recommendations, provided through a LMS, aimed to higher education students with some learning difficulties, particularly those who have dyslexia or reading difficulties.
Neuroscience and Biobehavioral Reviews Possible roles for fronto-striatal circuits in reading disorder

Several studies have reported hyperactivation in frontal and striatal regions in individuals with reading disorder (RD) during reading-related tasks. Hyperactivation in these regions is typically interpreted as a form of neural compensation related to articulatory processing. Fronto-striatal hyperactivation in RD could however, also arise from fundamental impairment in reading related processes, such as phonological processing and implicit sequence learning relevant to early language acquisition. We review current evidence for the compensation hypothesis in RD and apply large-scale reverse inference to investigate anatomical overlap between hyperactivation regions and neural systems for articulation, phonologi- cal processing, implicit sequence learning. We found anatomical convergence between hyperactivation regions and regions supporting articulation, consistent with the proposed compensatory role of these regions, and low convergence with phonological and implicit sequence learning regions. Although the application of large-scale reverse inference to decode function in a clinical population should be inter- pretedcautiously,ourﬁndingssuggestfuturelinesofresearchthatmayclarifythefunctionalsigniﬁcance of hyperactivation in RD.
Statistical on-line signature verification using rotation-invariant dynamic descriptors

The main challenges of online signature verification framework can be summarized in 1) accuracy and speed of preprocessing step, 2) the number of extracted features and how informative they are, and 3) complexity of decision making step owing to the less number of input samples for training. In this paper, in order to simplify the preprocessing step, a set of rotation invariant descriptors is presented. The complexity of system because of high dimensionality of signature features is reduced by exploiting the statistical moments of the wavelet coefficients. To improve it further, suggested Fisher-Metric(FM) filtering develops a feature selection algorithm. In the end, taking advantage of artificial neural network on SVC2004 database blow up the performance of the proposed approach with False Acceptance Rate (FAR) of 3% and False Rejection Rate (FRR) of 2.5% with an equal error rate (ERR) of less than 3%. The validation of the proposed framework suggests that the proposed method outperforms the state-of-the-art techniques.
Parameter-Free Incremental Co-Clustering for Multi-Modal Data in Cyber-Physical-Social Systems

With the rapid advances of cyber-physical-social systems (CPSS), large amounts of dynamic multi-modal data are being generated and collected. Analyzing those data effectively and efficiently can help to promote the development and improve the service quality of CPSS applications. As an important technique of multi-modal data analysis, co-clustering, designed to identify groupings of multi-dimensional data based on cross-modality fusion, is often exploited. Unfortunately, most existing co-clustering methods that mainly focus on dealing with static data become infeasible to fuse huge volume of multi-modal data in dynamic CPSS environments. To tackle this problem, this paper proposes a parameter-free incremental co-clustering method to deal with multi-modal data dynamically. In the proposed method, the single-modality similarity measure is extended to multiple modalities and three operations, namely, cluster creating, cluster merging, and instance partitioning, are defined to incrementally integrate new arriving objects to current clustering patterns without introducing additive parameters. Moreover, an adaptive weight scheme is designed to measure the importance of feature modalities based on the intra-cluster scatters. Extensive experiments on three real-world multi-modal datasets collected from CPSS demonstrate that the proposed method outperforms the compared state-of-the-art methods in terms of effectiveness and efficiency, thus it is promising for clustering dynamic multi-modal data in cyber-physical-social systems.
Modeling and evaluation for gather/scatter operations in Vector-SIMD architectures

Gather/scatter are state of the art vector memory access modes in Vector-SIMD architectures. However, because of the stochastic and complicated properties, the hardware design of gather/scatter operations lacks theoretical analysis and modeling. This paper proposes a model for gather/scatter operations on local vector memory for the first time. The model can not only give all the possible distributions of access locations, calculate the probability of access conflicts and predict the number of access conflicts, but also can provide the theoretical guidance for the performance optimization. This model is validated through experiments which can guide users to more specifically design and optimize the implementation of gather/scatter operations.
Profit sharing reducing the occurrences of accidents by predicted action-safety degree

This paper aims to reduce the number of occurrences of accidents during reinforcement learning(RL). RL is a powerful tool for autonomous acquisition of behavior without knowledge of dynamics of environment. However, because conventional algorithms require huge number of experiences of taking action to learn its goodness, the robots can not avoid having accidents during learning process. Such accidents may causes a breakdown and make the robots impossible to accomplish a task. Therefore, learning algorithm requires reducing the occurrence of accidents. This paper focuses on knowledge of robot designers to reduce the occurrences of accidents during the learning by using sensor information relating to accidents. This paper proposes a new PS-based algorithm which considers predicted action-safety degree to select, in order to reduce the occurrences of accidents during the learning. As a result, the effectiveness of proposed method was confirmed by simulation experiments.
The Benefit of Pro Re Nata Antiemetics Provided With Guideline-Consistent Antiemetics in Delayed Nausea Control

Background: Delayed nausea after chemotherapy remains a symptom of interest. Clinicians often provide additional and/or pro re nata (p.r.n.) antiemetics with guideline-consistent antiemetics in an attempt to achieve further symptom control. Whether the usage of additional and/or p.r. n. antiemetics provide added benefit remains as a question. Objective: The purpose of this study was to determine the benefit of providing additional antiemetics and/or p.r. n. antiemetics with guideline-consistent antiemetics in the control of nausea, functioning, and quality of life (QOL). Methods: A secondary data analysis of 143 breast cancer patients who received guideline-consistent antiemetics for anthracycline and cyclophosphamide chemotherapy was conducted. Instruments included vomiting frequency and nausea intensity items of the Multinational Association of Supportive Care in Cancer Antiemesis Tool and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–C30. Results: Providing p.r. n. antiemetics alleviated the incidence of significant nausea (intensity, ≥3) in the delayed phase. The experience of nausea contributed to symptom incidence in the next phase and cycle. The intensity of delayed nausea demonstrated a moderate negative correlation with physical and role functioning and global health/QOL. Conclusion: Providing patients with strategies to further control nausea (p.r.n. antiemetics) in addition to guideline-consistent antiemetics is recommended for practice. Because the nausea experience contributes to symptom incidence in the next phase and cycle and because the nausea intensity demonstrated a significant relationship with function and global health/QOL, proactive supportive approaches are strongly recommended. Implications for Practice: Delayed nausea control may be improved by adhering to guideline-consistent antiemetics and using p.r. n. antiemetics. Delayed nausea control would contribute to patients' functioning and QOL.
IoT platforms interoperability for active and assisted living healthcare services support

The paper proposes a novel approach to support interoperability between different IoT platforms with a particular focus on the support of Active and Assisted Living (AAL) healthcare services and applications. A novel IoT-based healthcare platform, supporting decentralized and mobile monitoring of assisted livings in highly heterogeneous contexts, can be obtained by integrating two already existing heterogeneous, non-interoperable IoT platforms (BodyCloud and universAAL) according to the strategy proposed in the ongoing INTER-IoT (Horizon 2020) European project. A specific healthcare assisted living use case, named INTER-Health, is deployed to both validate the INTER-IoT interoperability strategy and provide value added assisted living mobile healthcare services in terms of faster detection and correction of wrong lifestyles or critical situations.
Discontinuous fluids

At interfaces between different fluids, properties such as density, viscosity, and molecular cohesion are discontinuous. To animate small-scale details of incompressible viscous multi-phase fluids realistically, we focus on the discontinuities in the state variables that express these properties. Surface tension of both free and bubble surfaces is modeled using the jump condition in the pressure field; and discontinuities in the velocity gradient field. driven by viscosity differences, are also considered. To obtain derivatives of the pressure and velocity fields with sub-grid accuracy, they are extrapolated across interfaces using continuous variables based on physical properties. The numerical methods that we present are easy to implement and do not impact the performance of existing solvers. Small-scale fluid motions, such as capillary instability, breakup of liquid sheets, and bubbly water can all be successfully animated.
Empirical mode decomposition template matched filter for detection and estimation of T-wave alternans

T-wave alternans (TWA) is a well-recognized marker for malignant cardiac arrhythmia that may lead to sudden cardiac death. TWA detection and estimation becomes a challenge under the presence of process noises, transient outliers and physiological artifacts. We propose an empirical mode decomposition (EMD) based template matched filter approach for TWA analysis, targeting detection of distributed alternans energy rather than peak amplitude. EMD is performed to construct the template for matched filter which is central to alternans estimation and detection. The template is dynamically generated through decomposition into intrinsic mode functions (IMFs) and subsequent reconstruction. Notable improvement is achieved through correlation based reconstruction that shows a performance gain of > 2dB over other approaches including the spectral method (SM).
Developing an interlingual translation lexicon using WordNets and Grammatical Framework

The Grammatical Framework (GF) offers perfect translation between controlled subsets of natural languages. E.g., an abstract syntax for a set of sentences in school mathematics is the interlingua between the corresponding sentences in English and Hindi, say. GF “resource grammars” specify how to say something in English or Hindi; these are reused with “application grammars” that specify what can be said (mathematics, tourist phrases, etc.). More recent robust parsing and parse-tree disambiguation allow GF to parse arbitrary English text. We report here an experiment to linearise the resulting tree directly to other languages (e.g. Hindi, German, etc.), i.e., we use a language independent resource grammar as the interlingua. We focus particularly on the last part of the translation system, the interlingual lexicon and word sense disambiguation (WSD). We improved the quality of the wide coverage interlingual translation lexicon by using the Princeton and Universal WordNet data. We then integrated an existing WSD tool and replaced the usual GF style lexicons, which give one target word per source word, by the WordNet based lexicons. These new lexicons and WSD improve the quality of translation in most cases, as we show by examples. Both WordNets and WSD in general are well known, but this is the first use of these tools with GF.
Distributed Power Control in Downlink Cellular Massive MIMO Systems

This paper compares centralized and distributed methods to solve the power minimization problem with quality-of-service (QoS) constraints in the downlink (DL) of multi-cell Massive multiple-input multiple-output (MIMO) systems. In particular, we study the computational complexity, number of parameters that need to be exchanged between base stations (BSs), and the convergence of iterative implementations. Although a distributed implementation based on dual decomposition (which only requires statistical channel knowledge at each BS) typically converges to the global optimum after a few iterations, many parameters need to be exchanged to reach convergence.
An approach for analysis and identification of Raga of Flute Music using Spectrogram

This work presents an approach for identification of Raga of Flute music. Identification of Raga plays a vital role in learning about the rules in playing a composition, determining the valid Swaras, tunes and deflections. Experts can identify the Raga easily, learners cannot. The earlier works in this direction have dealt with Human Voice and simple Musical Instruments other than Flute, which could not address the versatility of source instrument or the Vocalist. In this work, application of Spectrogram for Raga analysis has been proposed which addresses the issues in the existing methods. The performance evaluation of the proposed method was evaluated on a database consisting of 32 Ragas using the statistical measures such as True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN). The test consists of 40 samples with complete scale of each raga and obtained TP of 90%, FN of 0%, TN of 100% and FP of 10%. Hence, the Accuracy of the proposed method is 95%.