Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rough Set Based Hybrid Method to Feature Selection

Features selection is a process to find the optimal subset of features that satisfy certain criteria. The aim of feature selection is to remove unnecessary features to the target concept. This paper investigates some basic concepts of rough set theory and ant colony optimization. Based on these studies, a hybrid approach to feature selection on combination of ant colony optimization and rough set theory is proposed. Experimental results obtained show this hybrid approach is a promising method for feature selection.
The Relationship of Symptoms and Neurocognitive Performance to Perceived Recovery From Sports-Related Concussion Among Adolescent Athletes

Sports medicine practitioners often consider athletes' self-reports of recovery for the management of concussion, and it is not clear which factors (i.e., neurocognitive performance and symptoms) athletes consider when forming perceptions of recovery from concussion. The current study assessed the relationship of perceptions of recovery to neurocognitive performance on the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) battery and to symptoms using the Post-Concussion Symptom Scale (PCSS). A total of 101 concussed athletes (62 males, 39 females) aged 12 to 18 years old were included in the study (M age = 14.75, SD = 1.76). Athletes were asked to rate their “percent back to normal” (i.e., perception of recovery) at the time of evaluation. A multiple regression for neurocognitive performance and symptoms revealed a significant model that accounted for 58% of the variance in perceptions of recovery. Adolescent athletes base their perceptions primarily on somatic symptoms (e.g., headache, nausea, vomiting, etc.), and these perceptions may be incongruent with objective neurocognitive measures. Athletes' tendency to overlook several factors when forming their perceptions of recovery should caution the sports medicine practitioner from relying on self-reported symptoms as their primary criterion for return-to-play decisions. These data further support the need for valid and reliable measures for concussion management.
RainbowNet : Color Extrapolation from Grayscale Images

Inspired by digital recoloring of historical photos and Zhang et al.[1], our project aims to train a convolutional neural network to colorize black-and-white images, optionally augmenting with embeddings obtained from Google’s Inception ResNet v2 (IRN2)[2]. Given a batch of RGB images, we generate a possible colorization by training and predicting in the CIE Lab color space. We additionally analyze our training data to examine which distributions of colors most often occur together via K-clustering. Our model predicts mostly browns, blues, and reds and performed better without the added complexity of the IRN2 fusion.
Deep Mantle Seismic Modeling and Imaging

Detailed seismic modeling and imaging of Earth’s deep interior is providing key information about lower-mantle structures and processes, including heat flow across the core-mantle boundary, the configuration of mantle upwellings and downwellings, phase equilibria and transport properties of deep mantle materials, and mechanisms of core-mantle coupling. Multichannel seismic wave analysis methods that provide the highest-resolution deep mantle structural information include network waveform modeling and stacking, array processing, and 3D migrations of P- and S-wave seismograms. These methods detect and identify weak signals from structures that cannot be resolved by global seismic tomography. Some methods are adapted from oil exploration seismology, but all are constrained by the source and receiver distributions, long travel paths, and strong attenuation experienced by seismic waves that penetrate to the deep mantle. Large- and small-scale structures, with velocity variations ranging from a fraction of a percent to tens of percent, have been detected and are guiding geophysicists to new perspectives of thermochemical mantle convection and evolution.
Facing the Trauma: How the Face Reveals and Processes Unexpressed Suffering

Emerging research in the neurosciences has stimulated a creative interdisciplinary approach to trauma psychotherapy, a new paradigm (Schore, 2009a). Psychotherapists, faced with copious amounts of information related to the mind, brain, and body, struggle to integrate this new knowledge into clinical practice. An integration of emerging neuropsychological theories including attachment theory (Main & Hesse, 2000, Schore, 2008; Tronick, 2007), polyvagal theory (Porges, 2009a), trauma (Schore, 2002; Levine, 1996; Ogden, 2006; van der Kolk, 1996) and neurological fixed action patterns (Llinas, 2001), combined with traditional wisdom creates a strong theoretical scaffolding for the growth of new clinical interventions. Face-to-face encounters co-construct the intersubjective field and support processing of deep visceral sensations, feelings, and emotions. The metaphor of the polyvagal nervous system provides a theoretical frame of orientation for an embodied intersubjective therapeutic relationship and right-hemispheric processing of trauma using somatic psychotherapeutic practices. This article offers, as a response to the emerging new theoretical paradigm, a clinical perspective into a somatic model of trauma resolution with a brief case vignette.
Characterization of a recombinant vaccinia virus expressing human melanoma-associated antigen p97

p97 is a cell surface glycoprotein expressed at high levels in most human melanomas but present only in trace amounts in normal adult tissues. We are interested in exploring the possibility of using recombinant vaccinia virus to express a specific tumor-associated antigen as a vaccine against human cancer. To this end, we constructed a recombinant virus, v-p97NY, which contains the entire coding sequence for p97 under the control of the vaccinia virus 7.5K promoter. Upon infection of tissue culture cells, v-p97NY expressed high levels of a membrane-bound glycoprotein immunoreactive with a p97-specific monoclonal antibody. Immunization of mice with this recombinant elicited high-titered antibodies against p97. Spleen cells isolated from these mice proliferated in vitro when stimulated either with purified p97 protein or with syngeneic cells expressing p97 antigen. Delayed-type hypersensitivity was also observed in immunized mice after challenge with p97-expressing cells. These findings indicate the potential usefulness of v-p97NY and similar recombinants in tumor immunotherapy.
Factors Underlying Caregiver Stress in Frontotemporal Dementia and Alzheimer’s Disease

Background: Frontotemporal dementia (FTD) has devastating effects on patients and caregivers, yet the factors underlying caregiver stress are poorly understood. Methods: 108 caregivers (79 = FTD, 29 = Alzheimer’s disease) participated in a postal survey. Self-report measures of perceived stress, depression, social networks, as well as patient-based measures of behaviour change and activities of daily living were completed. Results: Depression was a cardinal feature in FTD caregivers, and it accounted for more than 58% of stress scores. Both depression and stress were significantly higher than in AD. Neither the severity of behaviour changes nor functional disability explained caregiver stress. Conclusion: Caregiver stress is a multidimensional construct, and FTD caregivers should receive more support than currently available. Depression plays a key role in coping ability.
Integrated pictorial mnemonics and stimulus fading: Teaching kindergartners letter sounds.

BACKGROUND The conclusion from a vast literature on literacy acquisition is that letter knowledge is one of the best predictors of literacy development. The question of the best way to teach children letter sounds has not, as yet, been answered satisfactorily. AIMS The aim of this study was the evaluation of a computer training program using integrated-picture mnemonics combined with a fading procedure to teach children letter sounds. SAMPLE Thirty-nine kindergartners attending mainstream primary education participated in this study. METHOD A within-subject design was used. Each kindergartner learned letters under three conditions: (a) a fading condition in which letters are taught using a picture-supported first-sound mnemonics procedure in combination with a fading procedure; (b) an embedded condition in which letters are taught using the picture-supported first-sound-mnemonics procedure only and (c) a without-picture condition in which letters are taught using a first-sound procedure without-picture support. Dependent measures included a productive and receptive letter-sound test, and a first-sound isolation task. RESULTS Productive letter-sound knowledge in the fading condition was better than in the other two conditions. In addition, kindergartners with good and those with poor first-sound isolation ability performed equally well in the fading condition. However, in the embedded and in the without-picture conditions, the kindergartners with good first-sound isolation ability outperformed those with poor isolation ability. CONCLUSION These findings indicate that an integrated-picture mnemonics procedure combined with a fading procedure is effective in teaching kindergartners letter sounds and that the success of such a procedure does not depend on their initial first-sound isolation ability.
Modeling and Control of a Nonuniform Vibrating String Under Spatiotemporally Varying Tension and Disturbance

In this paper, robust adaptive boundary control is developed for a class of flexible string systems under unknown spatiotemporally varying distributed disturbance and time-varying boundary disturbance. The vibrating string is nonuniform since the spatiotemporally varying tension applied to the system. The nonuniform vibrating string system is represented by a nonlinear nonhomogeneous partial differential equation (PDE) and two ordinary differential equations (ODEs). Model-based control is first proposed at the right boundary of the string to suppress the vibration of the flexible nonuniform string system. To compensate for the system parametric uncertainties, robust adaptive boundary control is developed. With the proposed control, the uniformly ultimate boundness of the closed-loop system is demonstrated via Lyapunov's direct method. The state of the nonuniform string system is proven to converge to a small neighborhood of zero by appropriately choosing the design parameters. Simulations are provided to illustrate the effectiveness of the proposed control.
A coherence-matched linear source mechanism for subsonic jet noise

We investigate source mechanisms for subsonic jet noise using experimentally obtained datasets of high-Reynolds-number Mach 0.4 and 0.6 turbulent jets. The focus is on the axisymmetric mode which dominates downstream sound radiation for low polar angles and the frequency range at which peak noise occurs. A linearized Euler equation (LEE) solver with an inflow boundary condition is used to generate single-frequency hydrodynamic instability waves, and the resulting near-field fluctuations and far-field acoustics are compared with those from experiments and linear parabolized stability equation (LPSE) computations. It is found that the near-field velocity fluctuations closely agree with experiments and LPSE computations up to the end of the potential core, downstream of which deviations occur, but the LEE results match experiments better than the LPSE results. Both the near-field wavepackets and the sound field are observed directly from LEE computations, but the far-field sound pressure levels (SPLs) obtained are more than an order of magnitude lower than experimental values despite close statistical agreement of the near hydrodynamic field up to the potential core region. We explore the possibility that this discrepancy is due to the mismatch between the decay of two-point coherence with increasing distance in experimental flow fluctuations and the perfect coherence in linear models. To match the near-field coherence, experimentally obtained coherence profiles are imposed on the two-point cross-spectral density (CSD) at cylindrical and conical surfaces that enclose near-field structures generated with LEEs. The surface pressure is propagated to the far field using boundary value formulations based on the linear wave equation. Coherence matching yields far-field SPLs which show improved agreement with experimental results, indicating that coherence decay is the main missing component in linear models. The CSD on the enclosing surfaces reveals that the application of a decaying coherence profile spreads the hydrodynamic component of the linear wavepacket source on to acoustic wavenumbers, resulting in a more efficient acoustic source.
Model-based fault detection for the DELFI-N3XT Attitude Determination System

The Delfi-n3Xt nanosatellite is the second Dutch university satellite currently being developed at the Delft University of Technology. In its design, the Attitude Determination System (ADS) will be pivotal for optimal power point tracking to adequately provide the energy needed for normal operation and charging of the batteries. In this paper we explore a fault detection mechanism for the ADS based on the Unscented Kalman Filter (UKF) state estimator which has been successfully integrated into the simulation and modelling environment. The UKF provides a more computationally efficient estimator than traditional Kalman Filter variants. Faults introduced in the system include changes in the noise model and stuck-at-0 faults, resulting in disturbances in the output of the filter. Parameters of the filter are varied and the behaviour of the outcoming residuals is analyzed to evaluate its effectiveness in the detection of these errors.
Correlation between Anatomy and Root Canal Topography of First Maxillary Premolar on Kosovar Population

Aim: In this in vitro study the variation of root anatomy and canal system of the first human maxillary premolar was evaluated. Materials and Methods: Two hundred and twenty one maxillary first premolars (#221 teeth) were examined. All of the teeth were identified using the accepted criteria of Woelfel as maxillary first premolars. Selected teeth are analyzed using operating microscope (OP) and computed dental radiography (CDR). Results: There was a higher incidence of two-roots form 70.14% (n = 155), 21.72% (n = 48) had one root, and 8.14% (n = 18) had three roots. In the two-root category, 16.29% (n = 36) had bifurcation on apical third, 21.72% (n = 48) had bifurcation on middle third, and 32.13% (n = 71) had bifurcation on cervical third. The examination of root canal systems of the teeth was based on Vertucci’s classification type of canal: 64.58% (#31) of the cases had type IV (2-2-2), 25.00% (#12) type II (2-2-1), 8.33% (#4) type I (1-1-1), and 2.09% (#1) type III (1-2-1). Conclusion: This study showed a high incidence of two-rooted maxillary first premolars collected from different regions in Kosovo. Internal root canal system morphology reflects the external root anatomy. Furthermore, there is correlation between the shape of the outer surface of the root and the shape of the root canal. The cases with one root of the maxillary first premolar with a deep depression on the mesial side contain a double root canal system more often than a single canal.
Semi-analytical finite elements methods for dispersion curves using higher order elements for long range ultrasonic testing

Guided waves have great potential as a NDE method. For this purposes it is very important to obtain the dispersion curves which depend on the cross-section of the structures in the guide waves. For the case of an arbitrary cross-section semi analytical finite element methods are developed. This paper describes the use of different node elements in the performance of the SAFEM algorithm using high order elements.
G20210A prothrombin gene mutation identified in patients with venous leg ulcers

The G20210A mutation variant of prothrombin gene is the second most frequent mutation identified in patients withdeep venous thrombosis, after factor V Leiden. The risk for developing deep venous thrombosis is high in patients identified as heterozygous for G20210A mutation. In order to identify this polymorphism in the gene coding prothrombin, the 345bp fragment in the 3′‐ untranslated region of the prothrombin gene was amplified using amplification by polymerase chain reaction and enzymatic digestion by HindIII (restriction endonuclease enzyme). The products of amplification and enzymatic's digestion were analized using agarose gel electrophoresis. We investigated 20 patients with venous leg ulcers and we found 2 heterozygous (10%) for G20210A mutation. None of the patients in the control group had G20210A mutation. Our study confirms the presence of G20210A mutation in the Romanian population. Our study also shows the link between venous leg ulcers and this polymorphism in the prothrombin gene.
The SIGMA-1 dataflow computer

SIGMA-1 is the most exciting practical computer prototype with dataflow architecture. The project aims to outperform conventional von Neumann computers. This large-scale dataflow computer, for scientific and technological computations, is in the final stage of the design and construction phase at the Electrotechnical Laboratory, Ministry of International Trade and Industry. This paper overviews the system architecture of the SIGMA-1, and discusses the performance measurements made recently. The architectural issues in dataflow computing machines and the design decisions made in the SIGMA-1 project in achieving 100 MFLOPS performance are presented. The history of the SIGMA-1 project is also outlined.
Rule-based Expert Systems for Selecting Information Systems Development Methodologies

Information Systems (IS) are increasingly becoming regarded as crucial to an organization's success. Information Systems Development Methodologies (ISDMs) are used by organizations to structure the information system development process. ISDMs are essential for structuring project participants' thinking and actions; therefore ISDMs play an important role to achieve successful projects. There are different ISDMs and no methodology can claim that it can be applied to any organization. The problem facing decision makers is how to select an appropriate development methodology that may increase the probability of system success. This paper takes this issue into account when study ISDMs and provides a Rule-based Expert System as a tool for selecting appropriate ISDMs. The proposed expert system consists of three main phases to automate the process of selecting ISDMs. Three approaches were used to test the proposed expert system. Face validation through six professors and six IS professionals, predictive validation through twenty four experts and blind validation through nine employees working in IT field. The results show that the proposed system was found to be run without any errors, offered a friendly user interface and its suggestions matching user expectations with 95.8%. It also can help project managers, systems' engineers, systems' developers, consultants, and planners in the process of selecting the suitable ISDM. Finally, the results show that the proposed Rule-based Expert System can facilities the selection process especially for new users and non- specialist in Information System field.
Diagnostic stability 18 months after treatment initiation for first-episode psychosis.

OBJECTIVES (1) Assessment of diagnostic stability of psychotic disorders or psychotic mood disorders from 6 weeks to 18 months after initiation of treatment in a representative first-episode psychosis (FEP) sample. (2) Comparison between those patients who shifted from DSM-IV schizophreniform disorder to schizophrenia or schizo-affective disorder and those whose diagnosis of schizophreniform disorder remained stable. METHOD The Early Psychosis Prevention and Intervention Centre (EPPIC) in Australia admitted 786 FEP patients from January 1998 to December 2000. Data were collected from patients' medical records (MRs) using a standardized questionnaire. Seven hundred four MRs were available, 36 of which were excluded owing to nonpsychotic diagnoses or a psychotic disorder due to a general medical condition. Of the remaining 668 patients, 176 (26.3%) were lost to follow-up. Four hundred ninety-two subjects were analyzed. Strategies to assure validity and reliability of diagnoses were applied. RESULTS The same diagnosis was made at baseline (< or = 6 weeks after admission into EPPIC) and 18 months for 69.9% of the patients. Among the most consistent diagnoses were schizophrenia (97.3%), schizoaffective disorder (94.1%), and bipolar disorder (83.2%); the least stable, as expected, was schizophreniform disorder (40.0%). In subjects with schizophreniform disorder at baseline, the best predictors of a shift from schizophreniform disorder to schizophrenia or schizoaffective disorder were a higher baseline Clinical Global Impressions-Severity of Illness scale score and lower premorbid Global Assessment of Functioning score, although the variance accounted for was small (R2 = .07). CONCLUSIONS A longitudinally based diagnostic process in FEP samples is needed, especially in schizophreniform disorder and bipolar disorder. However, a thorough initial assessment of patient and family by a specialized team of investigators regarding the kind and duration of patient symptoms may lead to high diagnostic stability, especially in schizophrenia and schizoaffective disorder, even in a FEP sample with a relatively short duration of untreated psychosis.
A hybrid Particle Swarm Optimization algorithm for combinatorial optimization problems

Particle Swarm Optimization (PSO) belongs to a class of algorithms inspired by natural social intelligent behaviors, called Swarm Intelligence (SI). PSO has been successfully applied to solve continuous optimization problems, however, its potential in discrete problems has not been sufficiently explored. Recent works have proposed hybridization of PSO using local search and Path relinking algorithms with promising results. This paper aims to present a hybrid PSO algorithm that uses local search and Path relinking too, but differently to the previous approaches, this works maintains the main PSO concept for the update of the velocity of the particle. The paper describes the proposed algorithm and a set of experiments with the Traveling Salesman Problem (TSP). The results are compared to other Particle Swarm Optimization algorithms presented previously for the same problem. The results are encouraging and reinforce the idea that PSO algorithms can also provide good results when dealing with discrete problems.