Miracle-dz/UniFAR-FTUs huggingface dataset:
"Mini-open" Repair for Acute Achilles Tendon Ruptures

ABSTRACT The conventional exposure for surgical repair of acute Achilles tendon ruptures affords ample visualization to ensure anatomic apposition of the disrupted tendon fibers. Reported outcomes of this standard approach to Achilles tendon repair are generally favorable. However, such broad exposure with extensive iatrogenic disruption of the subcutaneous tissues and paratenon increases the potential for peritendinous adhesions. In an attempt to diminish the risk of such adhesions without compromising the functional outcome, the "mini-open" technique was developed. Despite the minimally invasive nature of the procedure, anatomic apposition of the disrupted tendon can be consistently performed. The mini-open repair represents an attractive compromise between the nonoperative, pure percutaneous, and traditional operative treatment alternatives.
Neuro-adaptive consensus tracking of multiagent systems with a high-dimensional leader and directed switching topologies

This brief addresses the practical consensus tracking problem for multiagent systems (MASs) with a high-dimensional leader and uncertain dynamics over directed switching topologies based on the neuro-adaptive approach. First, an observer is embedded in each follower to estimate the leader’s state. And it is proven that the observer will track the leader asymptotically if the average dwell time (ADT) is sufficiently large. Furthermore, a discontinuous feedback controller plus a compensation controller is designed for each follower such that the error between the followers’ states and their associated observers’ states will converge into a bounded set. It is worth noting that the obtained residual error depends neither on the neural network (NN) approximation error nor on the external disturbances. Finally, a simulation is performed to validate the obtained theoretical result.
Predictive Maintenance System using motor current signal analysis for Industrial Robot

Predictive Maintenance (PdM) is one of the key enabling technologies in Industry 4.0. The Factories of the Future will adopt highly automated and interconnected environment where predictive fault detection will have an essential role to ensure efficient and reliable industrial operations. Due to their high efficiency and their low cost Cartesian Robots (CRs) represent one of the widely used automation systems in industry. Their movements and efficiency depends on transmission system and its degradation. However not much has been done in terms of PdM for these robots and very few works tries to deal with this problems. Different failures for those kind of robots are attributable to the transmission system. This work details the effect of the transmission system on the robot electrical actuation according to Motor Current Signal Analysis (MCSA) theory. This analysis propose different tools, used in others disciplines for different purposes, to infer features of the faulty condition. By monitoring the motor current of the CR, after a signal preprocessing, a proper fault index have been investigated in order to detect the functionality state of the transmission system. The preliminary results obtained are encouraging compared to classic spectral analysis. The monitoring and analysis have also been extended to the transient state. All the fault detection tests have been carried out directly on the electric drive mounted on a real industrial CR.
Remaining Useful Life Prediction Using Time-Frequency Feature and Multiple Recurrent Neural Networks

This paper presents a new framework to improve processing time and accuracy for remaining useful life (RUL) prediction of a degrading equipment. Most of the existing machine learning-based RUL prediction approaches are often not robust to noise and outliers, requiring a large overhead processing to select best features and a complex procedure to build an accurate model. This paper explores using signal decomposition methods to eliminate the noise, and multiple deep learning techniques to minimize the outliers for a fast and accurate RUL prediction system. Specifically, we leverage either the wavelet transform (WT) or empirical mode decomposition (EMD) to extract time-frequency domain features, then build an ensemble deep neural network model that jointly utilize three different recurrent neural networks (RNNs) to predict the RUL of a degrading equipment. We evaluate the our proposed approach using aircraft gas turbine engine data from NASA, to show the effects of WT and EMD on the RUL prediction performance in terms of processing time and RUL accuracy. Experimental results demonstrate that our proposed approach significantly outperforms existing approaches.
Classifier Training from a Generative Model

We investigate the samples derived from generative adversarial networks (GAN) from a classification perspective. We train a classifier on generated samples and on real data and see how they compared on a held out validation set. We see that recent GAN models which produce visually convincing samples are not yet able to match the training on real data. To analyse this we compare training a classifier on generated samples and various sizes of the real training set. We propose architectural and algorithmic changes to reduce this gap. First, we show that a modification to the GAN architecture is needed, which leads to improve generation of samples. Second, we use multiple GAN models as a way to cover the real data distribution, again leading to improvement in classifier training. We also show that in the case of training on small number of samples, a GAN model provides better compression in terms of storage requirements as compared to the real data.
Multi-Step Community Evolution Prediction Methods via Marcov Chain and Classifier Chain

As social network analysis becomes a popular topic, more and more people show interest in the network community evolution in the future. Currently existing algorithms usually use machine learning methods to predict the future event that a community will undergo. However, these algorithms can't achieve multi-step prediction. This paper proposes two novel algorithms named MC-based prediction and CC-based multi-step prediction. The first one is carried out based on Marcov Chain (MC). In addition to performing multi-step prediction, the MC algorithm is able to achieve higher single-step prediction accuracy than the algorithms using machine learning methods in some cases. The second algorithm proposed in this paper applies the thought of Classifier Chain (CC) to multi-step prediction. Experiments show that these two algorithms are valid and have good performance for multi-step prediction.
HPC Simulations of a Reverberation Chamber with Nonparallel Walls

The introduction of nonparallel walls for the chamber geometry destroys the regular chamber modes in the sub spectrum of waves eigenmodes, responsible for non-ergodicity and energy localization. This allows to enhance field chaoticity inside the RC. The optimization of an FDTD code for high performance computing architectures is essential for an accurate study of geometry variation effects on the chamber performance. In particular, we compute the number of uncorrelated stirrer positions by means of a multivariate approach whose results are compared to those provided by traditional autocorrelation function methods. The benefits given by the using of nonparallel walls are evident in the increment of total number of uncorrelated stirrer angles and on the elimination of frequencies where the uncorrelation drops due to energy localization.
Understanding Development Process of Machine Learning Systems: Challenges and Solutions

Background: The number of Machine Learning (ML) systems developed in the industry is increasing rapidly. Since ML systems are different from traditional systems, these differences are clearly visible in different activities pertaining to ML systems software development process. These differences make the Software Engineering (SE) activities more challenging for ML systems because not only the behavior of the system is data dependent, but also the requirements are data dependent. In such scenario, how can Software Engineering better support the development of ML systems? Aim: Our objective is twofold. First, better understand the process that developers use to build ML systems. Second, identify the main challenges that developers face, proposing ways to overcome these challenges. Method: We conducted interviews with seven developers from three software small companies that develop ML systems. Based on the challenges uncovered, we proposed a set of checklists to support the developers. We assessed the checklists by using a focus group. Results: We found that the ML systems development follow a 4-stage process in these companies. These stages are: understanding the problem, data handling, model building, and model monitoring. The main challenges faced by the developers are: identifying the clients’ business metrics, lack of a defined development process, and designing the database structure. We have identified in the focus group that our proposed checklists provided support during identification of the client’s business metrics and in increasing visibility of the progress of the project tasks. Conclusions: Our research is an initial step towards supporting the development of ML systems, suggesting checklists that support developers in essential development tasks, and also serve as a basis for future research in the area.
THE IMPLEMENTATION OF SOLUTION-FOCUSED BRIEF THERAPY (SFBT) WITH AT-RISK YOUTH IN AN ALTERNATIVE SCHOOL ENVIRONMENT

OF DISSERTATION THE IMPLEMENTATION OF SOLUTION-FOCUSED BRIEF THERAPY (SFBT) WITH AT-RISK YOUTH IN AN ALTERNATIVE SCHOOL ENVIRONMENT Research indicates the potential utility of schools as sites for service delivery of mental health interventions. The application of solution-focused brief therapy (SFBT) within the school domain is reflected in the child psychotherapy literature. Findings on the use of SFBT in school settings suggest that it may be well suited to school contexts given its time-efficient, goal-directed, and strengths-based behavioral approach. The primary purpose of this study was to determine the effectiveness of SFBT with at-risk youth in an alternative school setting. The researcher utilized a multiple case study design to examine the impact of a 6-session SFBT intervention on adolescent behavioral outcomes. Six students were randomized to one of three baseline conditions and received the SFBT intervention following baseline data collection. Data were obtained from multiple raters at baseline, posttest, and 6-week follow-up. In addition, students completed self-reported ratings at the beginning of each SFBT intervention session. Data were evaluated using non-regression approaches and visual analyses. Preliminary results indicated that four out of six students exhibited reliable change (6-point increase in post-ORS mean scores), and four out of the six students demonstrated clinically significant change (baseline ORS mean scores below the adolescent clinical cutoff of <28). Results also indicated a decrease in total problem behavior scores at posttest for all informants on a normed assessment of emotional and behavioral functioning. Follow-up data were collected for four out of six students, and results suggested that this decrease in ratings was maintained or decreased further across all raters for three out of the four student participants. Overall, preliminary results indicated the potential utility of SFBT with at-risk youth in an alternative school environment. Strengths and limitations of the current study, as well as additional research aims (e.g., impact of therapist alliance, fidelity monitoring in SFBT) and future research areas are also presented.
Accuracy Performance Evaluation Method of Integrated Navigation System Based on two-level FCE

In order to evaluate the accuracy performance of the integrated navigation system, a set of evaluation index system based on device-level error is proposed in this paper, which uses a two-level fuzzy comprehensive evaluation (two-level FCE) method. Analytic hierarchy process (AHP) and entropy weighting method are used to determine the combination weights of device layer combination in the comprehensive evaluation method. Firstly, the source data is converted into percentile data by semi-ridge function. Then, the membership degree is determined by normal distribution. The characteristics of normal distribution parameters are analyzed, which is helpful for the rational selection of distribution parameters. Finally, three groups of integrated navigation systems are evaluated to verify the effectiveness of the two-level fuzzy comprehensive evaluation method. Experiment results show that the proposed method can provide good technical supports for the follow-up research.
Machine Learning-based Silence Detection in Call Center Telephone Conversations

This study presents the development of a voice activity detection (VAD) system tested on call center telephony data obtained from our local site. The concept of bag of audio words (BoAW) combined with a naive Bayes classifier was applied to achieve the task. It was formulated as a binary classification problem with speech as the positive class and silence/background noise as the negative class. All the processing was performed on the Mel-frequency cepstral coefficients (MFCCs) extracted from the audio recordings. The results which are presented as accuracy score and receiver operating characteristics (ROC) indicate an excellent performance of the developed model. The system is to be deployed within our call center to aid data analysis and improve overall efficiency of the center.
Real-time Safe Navigation Strategy for Mobile Robots in 3D:A Deforming Path Approach

This work considers the 3D safe navigation problem of autonomous vehicles such as flying drones and underwater vehicles. Such a problem is very challenging especially when dealing with unknown and/or dynamic obstacles. In these situations, the vehicle is required to take quick actions to avoid collisions with detected obstacles. Therefore, the proposed strategy adopts a sense and avoid paradigm to produce collision-free motions. This is achieved by applying real-time deformations to the vehicle’s reference path around obstacles based on sensory information which can provide better computational solutions and quicker actions compared with map-based navigation approaches. Also, quintic Bezier splines are utilized to generate smooth deformations. Computer simulations have been conducted to demonstrate the effectiveness of the proposed strategy with unknown/dynamic obstacles.
Zerumbone significantly improved immunoreactivity in the synovium compared to Channa striatus extract in monosodium iodoacetate (MIA)-induced knee osteoarthritis in rat

The main aim of this study was to compare the immunoreactivity of some osteoarthritis related neuropeptides following oral administration of two natural remedies that is Channa striatus extract and zerumbone against monosodium iodoacetate induced knee osteoarthritis changes in the rat’s synovial membrane. Assay of PGE2 and PGF2α in the serum were performed to evaluate their role during osteoarthritis events and post oral application of the treatment. Forty rats were divided equally into four groups. Rats in the first and second groups were received channa extract and zerumbone, respectively. Rats in the third group were treated with celecoxib, whereas the fourth group was treated with normal saline. Evaluation of immunoreactivity of the following neuropeptides: Protein gene product 9.5, calcitonin gene related peptide and neuropeptide Y in the synovial membranes was implemented with the aid of both histopathology and immunohistochemistry approaches. Results revealed lower pathology score in both first and second groups accompanied with markedly improved immunoreactivity in zerumbone treated groups compared to channa extract group. Significant different concentrations of PGE2 but not PGF2α were detected within studied groups. Both remedies significantly improved the immunoreactivity which appeared more apparent in the group treated with zerumbone. Prostaglandin E2 has a role in osteoarthritis development and regulation.
Pendulum as an Educational Remote Experiment

Pendulum motion as a prolific experimental scenario has been used by physics teachers and researchers at least since the 17th century. The variability of pendulum models allows the study of several mechanical phenomena and gives the opportunity to validate distinct mathematical features of the dynamical and kinematic event. These experimental setups could be used remotely, in a way it could teach physics to students in the pre-university and university level, regardless budget limitations of the particular institution accessing the experiment. The World Pendulum Alliance experimental network as a gravity mapper, presents a strong basis to address students in a non-conventional way, empowering them inside an inquiry learning space.
Performance of object recognition in wearable videos

Wearable technologies are enabling plenty of new applications of computer vision, from life logging to health assistance. Many of them are required to recognize the elements of interest in the scene captured by the camera This work studies the problem of object detection and localization on videos captured by this type of camera. Wearable videos are a much more challenging scenario for object detection than standard images or even another type of videos, due to lower quality images (e.g. poor focus) or high clutter and occlusion common in wearable recordings. Existing work typically focuses on detecting the objects of focus or those being manipulated by the user wearing the camera. We perform a more general evaluation of the task of object detection in this type of video, because numerous applications, such as marketing studies, also need detecting objects which are not in focus by the user. This work presents a thorough study of the well known YOLO architecture, that offers an excellent trade-off between accuracy and speed, for the particular case of object detection in wearable video. We focus our study on the public ADL Dataset, but we also use additional public data for complementary evaluations. We run an exhaustive set of experiments with different variations of the original architecture and its training strategy. Our experiments drive to several conclusions about the most promising directions for our goal and point us to further research steps to improve detection in wearable videos.
The Consequences of Missing Specification for Coupling-Decoupling Networks

Coupling-decoupling networks (CDN) are used in electromagnetic compatibility (EMC) conducted immunity testing according to IEC 61000-4-6. This standard defines the requirements for CDNs to achieve a good reproducibility [1]. However, during the analysis of a recent international interlaboratory comparison (proficiency testing) on conducted immunity testing [2], we observed some deviations in the participant results that could not be explained by evident reasons such as bad earth connection, broken cable or faulty setup. A careful analysis has demonstrated that CDNs from different manufacturers might behave significantly differently with given equipment under test (EUT), despite the fact that they are all compliant with the standard IEC 61000-4-6.
Motivational insights about first year engineering students experimenting active learning in mathematics courses

In Latin America, it is common to observe a lack of motivation in the students enrolled in mathematics engineering courses. We have been implementing different methodologies to teach such courses and studying their academic impact; however, we have not analyzed their effect on motivation. There are several studies centered in motivation and active learning, but only a few deals with mathematics courses in Latin America. In this paper, we present the results of a validated translation of the Motivated Strategies for Learning Questionnaire conducted to two groups of engineering students enrolled in a pre-calculus course, taught using active learning and Lecture-Based methodologies. Our findings include a significant difference in the intrinsic motivation between those groups. The results of the remaining sub-scales of the test in both groups are also discussed in this paper. To complete our analysis, we also compare the students' motivation between several subgroups: Traditional and Non-Traditional, men and women, among others.
Exergames and their benefits in the perception of the Quality of Life and Socialization on institutionalized older adults

Benefits of augmented reality games in physical and cognitive dimensions have been demonstrated before but not many works have addressed the social dimensions which are quite important for the overall quality of life. The objective of this work is to assess the impact of a recently developed platform for senior exercise with augmented reality games (exergames) in the perception of quality of life and socialization of older adults institutionalized in nursing homes. We describe a 3 months study with 18 participants (85.28 ± 6.02 years) divided into three groups of different functional abilities (Group-1 n=6, Group 2 n=5, Group 3 n=7), with a weekly session of exergames of approximately 90 to 120 minutes. Our study shows significant improvements under World Health Quality of Life Scale in domains like Social Relations and also in Friends Satisfaction domain of the Satisfaction with Social Support Scale. Differences between groups were also found regarding Satisfaction with Social Activities. Some positive and significant correlations were found between the number of group sessions attendance and the perceived quality of Social Relations and System Usability. We, thus, conclude that our exergame sessions have several benefits for quality of life perception and social relations.
Permissible SA and Radiant Exposure for Brief Exposure in GHz Region

The specific absorption rate (SAR) averaged over 10 g of tissue is used as a physical quantity to prevent local temperature elevation in the international guidelines for constant exposure to radio-frequency electromagnetic fields. The averaging time of SAR is set to 6 min for local exposure. The ICNIRP public consultation document introduced a new physical quantity, absorbed power density (APD), as the basic restriction above 6 GHz. In addition, the limit for the specific absorption (SA) and absorbed energy density (AED) were set to avoid heating for brief exposures (shorter than the averaging time of 6 min). If all the energy for the averaging time of 6 min for continuous wave are concentrated into short duration and multiple pulse exposures, the local temperature elevation for the pulse(s) may exceeds that of exposure at almost constant exposure although it satisfies the limit for the SAR/APD. The purpose of this study is to determine how best to set the metric in order to limit temperature rise from brief exposures. We first confirmed that the peak temperature elevation for brief pulse exceeds the steady-state temperature for continuous exposure. We then proposed the limit energy density from the regression curve depending on the exposure duration of brief-pulse(s). The time course of temperature elevation in the multi-layer cube model was computed for typical scenarios of multiple pulses exposures. The SA and AED based on the regression curve can be used as a metric to prevent excess temperature elevation for different brief exposure scenario.