Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis Applied 4th-Order Constant Absolute Bandwidth Frequency-Agile Bandpass Filter With Cross-Coupling

A novel 4th-order cross-coupled electrical tunable bandpass filter with constant absolute bandwidth and high frequency selectivity is proposed. The electric coupling coefficients and the magnetic coupling coefficients of the cross-coupled resonators and the external quality factor of the tunable filter are investigated. The design curves of coupling coefficients and external quality factor versus the resonant frequency are synthesized for the cross-coupled tunable filter. Tunable resonator with separate electric dominant and magnetic dominant coupling regions is designed to build the cross-coupled tunable filter topology. Theoretical analysis is derived to estimate the coupling coefficients and the performance of the proposed filter. A prototype is fabricated and measured. The measurement shows that the filter has a −3 dB bandwidth of 117 ± 3MHz from 0.95 to 1.48 GHz. Two cross-coupled transmission zeros beside the passband are generated, and the frequency selectivity is enhanced. The −30 dB/−3 dB bandwidth shape factor is 1.83~1.93.
Prevalence and Clinical Factors of Migraine in Patients With Spontaneous Coronary Artery Dissection

Background Spontaneous coronary artery dissection (SCAD) is a cause of acute coronary syndrome predominantly in women without usual cardiovascular risk factors. Many have a history of migraine headaches, but this association is poorly understood. This study aimed to determine migraine prevalence among SCAD patients and assess differences in clinical factors based on migraine history. Methods and Results A cohort study was conducted using the Mayo Clinic SCAD “Virtual” Multi‐Center Registry composed of patients with SCAD as confirmed on coronary angiography. Participant‐provided data and records were reviewed for migraine history, risk factors, SCAD details, therapies, and outcomes. Among 585 patients (96% women), 236 had migraine history; the lifetime and 1‐year prevalence of migraine were 40% and 26%, respectively. Migraine was more common in SCAD women than comparable literature‐reported female populations (42% versus 24%, P<0.0001; 42% versus 33%, P<0.0001). Among all SCAD patients, those with migraine history were more likely to be female (99.6% versus 94%; P=0.0002); have SCAD at a younger age (45.2±9.0 years versus 47.6±9.9 years; P=0.0027); have depression (27% versus 17%; P=0.025); have recurrent post‐SCAD chest pain at 1 month (50% versus 39%; P=0.035); and, among those assessed, have aneurysms, pseudoaneurysms, or dissections (28% versus 18%; P=0.018). There was no difference in recurrent SCAD at 5 years for those with versus without migraine (15% versus 19%; P=0.39). Conclusions Many SCAD patients have a history of migraine. SCAD patients with migraine are younger at the time of SCAD; have more aneurysms, pseudoaneurysms, and dissections among those imaged; and more often report a history of depression and post‐SCAD chest pain. Clinical Trial Registration URL: https://www.clinicaltrials. gov. Unique identifiers: NCT01429727, NCT01427179.
Neuromodulation and a Reconceptualization of Autism Spectrum Disorders: Using the Locus Coeruleus Functioning as an Exemplar

The Autism Spectrum Disorders (ASD) are a heterogeneous group of developmental disorders. Although, ASD can be reliably diagnosed, the etiology, pathophysiology, and treatment targets remain poorly characterized. While there are many atypical findings in anatomy, genetics, connectivity, and other biologic parameters, there remains no discreet hypothesis to explain the core signs as well as the very frequent comorbidities. Due to this, designing targets for treatments can only be done by assuming each symptom is a result of a discreet abnormality which is likely not the case. Neuronal circuity remains a major focus of research but rarely taking into account the functioning of the brain is highly dependent on various systems, including the neuromodulatory substances originating in the midbrain. A hypothesis is presented which explores the possibility of explaining many of the symptoms found in ASD in terms of inefficient neuromodulation using the functioning of the locus coeruleus and norepinephrine (LC/NE) as exemplars. The basic science of LC/NE is reviewed. Several functions found to be impaired in ASD including learning, attention, sensory processing, emotional regulation, autonomic functioning, adaptive and repetitive behaviors, sleep, language acquisition, initiation, and prompt dependency are examined in terms of the functioning of the LC/NE system. Suggestions about possible treatment directions are explored.
Feasibility Study of a Repetitive Inductive Pulsed Power Supply Circuit for Electromagnetic Rail-Guns

Superconducting inductive pulsed power supplies, which are used in electromagnetic rail-gun (EMRG) application, have attracted a lot of attention during recent years. In this paper, a repetitive inductive pulsed power supply circuit based on a high-temperature superconducting transformer is proposed to drive EMRG. The circuit is capable to recover the remaining energy of the system before the end of an electromagnetic launch. This is beneficial to eliminate the muzzle arc of EMRG and improve the energy transfer efficiency. In order to prove the feasibility, a 3-m-long simple EMRG model was used for simulation. The driving process of the circuit was analyzed and the output characteristics were studied. The simulation results show that the repetitive inductive pulse forming circuit is suitable for driving EMRG. Reasonable selection of circuit parameters can improve the launching performance of the system.
Active Learning in Data Science Education

This article about Active learning in data science education deals with methods and tools to support active learning of data science, in particular within the Knowledge Discovery subject at the Technical University of Kosice, Slovakia. One of the results of the work presented in this article is an implemented web application to support active learning in data science. The article involves a brief overview of the methods supporting the active learning which may be used also for teaching data science. The final web application which was proposed and tested by the users, was created by RShiny application. The supporting application is helpful mainly for the students as an interactive mean of active learning in data science. The result of the article is available and it was applied in practice. This work contributes to the effective learning of the users (learners) as well as it contributes to better understanding, comprehension and application of the acquired knowledge and experiences of the students in practice by means of active learning.
Opioids for Chronic Noncancer Pain: A Systematic Review and Meta-analysis

Importance Harms and benefits of opioids for chronic noncancer pain remain unclear. Objective To systematically review randomized clinical trials (RCTs) of opioids for chronic noncancer pain. Data Sources and Study Selection The databases of CENTRAL, CINAHL, EMBASE, MEDLINE, AMED, and PsycINFO were searched from inception to April 2018 for RCTs of opioids for chronic noncancer pain vs any nonopioid control. Data Extraction and Synthesis Paired reviewers independently extracted data. The analyses used random-effects models and the Grading of Recommendations Assessment, Development and Evaluation to rate the quality of the evidence. Main Outcomes and Measures The primary outcomes were pain intensity (score range, 0-10 cm on a visual analog scale for pain; lower is better and the minimally important difference [MID] is 1 cm), physical functioning (score range, 0-100 points on the 36-item Short Form physical component score [SF-36 PCS]; higher is better and the MID is 5 points), and incidence of vomiting. Results Ninety-six RCTs including 26 169 participants (61% female; median age, 58 years [interquartile range, 51-61 years]) were included. Of the included studies, there were 25 trials of neuropathic pain, 32 trials of nociceptive pain, 33 trials of central sensitization (pain present in the absence of tissue damage), and 6 trials of mixed types of pain. Compared with placebo, opioid use was associated with reduced pain (weighted mean difference [WMD], −0.69 cm [95% CI, −0.82 to −0.56 cm] on a 10-cm visual analog scale for pain; modeled risk difference for achieving the MID, 11.9% [95% CI, 9.7% to 14.1%]), improved physical functioning (WMD, 2.04 points [95% CI, 1.41 to 2.68 points] on the 100-point SF-36 PCS; modeled risk difference for achieving the MID, 8.5% [95% CI, 5.9% to 11.2%]), and increased vomiting (5.9% with opioids vs 2.3% with placebo for trials that excluded patients with adverse events during a run-in period). Low- to moderate-quality evidence suggested similar associations of opioids with improvements in pain and physical functioning compared with nonsteroidal anti-inflammatory drugs (pain: WMD, −0.60 cm [95% CI, −1.54 to 0.34 cm]; physical functioning: WMD, −0.90 points [95% CI, −2.69 to 0.89 points]), tricyclic antidepressants (pain: WMD, −0.13 cm [95% CI, −0.99 to 0.74 cm]; physical functioning: WMD, −5.31 points [95% CI, −13.77 to 3.14 points]), and anticonvulsants (pain: WMD, −0.90 cm [95% CI, −1.65 to −0.14 cm]; physical functioning: WMD, 0.45 points [95% CI, −5.77 to 6.66 points]). Conclusions and Relevance In this meta-analysis of RCTs of patients with chronic noncancer pain, evidence from high-quality studies showed that opioid use was associated with statistically significant but small improvements in pain and physical functioning, and increased risk of vomiting compared with placebo. Comparisons of opioids with nonopioid alternatives suggested that the benefit for pain and functioning may be similar, although the evidence was from studies of only low to moderate quality.
Indications for the surgical resection of stage IV disease

Tumor biology and careful patient selection weigh heavily in determining the appropriate role of surgical resection in stage IV melanoma. Historically, surgical resection for highly selected patients with metastatic melanoma was the only treatment modality associated with improved long‐term survival and the ability to provide palliation. With the new age of effective systemic therapies, the treatment of metastatic melanoma has become more intricate and future work is needed to better define the role for surgery within the current treatment paradigm.
CDM Based Virtual FMCW MIMO Radar Imaging at 79 GHz

In this paper, we will be using a 79 G Hz Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar and apply the Code Division Multiplexing (CDM) method to increase the number of elements virtually. This, in turn, enhances the angular resolution of the radar. The major contribution of our work comes from the fact that by exploiting the CDM method we will make it possible for multiple FMCW radars to operate in close proximity of each other. In fact, using coded signals the effect of interference can be alleviated considerably.
Integrating Deep Learning Approaches for Identifying News Reprint Relation

With the rapid development of big data and new media technologies, a large amount of original news is generated and reprinted on the Internet via news portals. Identifying news reprint relations is of great importance for the analysis of news diffusion patterns and copyright protection. However, the amount of news data on the Internet creates a huge challenge for efficiently identifying news reprint relation. Some existing studies focus on computing the similarity of the full text of news reports, which is not always effective, because some reprints only excerpt some sentences of the original news reports. The core challenge of improving identification accuracy is excavating the potential semantic relevance between news articles at the sentence level. Inspired by deep learning and semantic-based text representation models, this paper proposes an approach for identifying news reprint relation by integrating deep learning approaches. First, news reports that are not related to the topic of the original news report are removed via topic correlation mining. Then, the potential semantic relevance is excavated at the sentence level through the integration of semantic analysis methods, and reprint relations are identified between news reports. The performance of the approach is empirically evaluated using a real-world dataset. Experimental results show that the semantic analysis model integration allows us to mine in-depth semantic associations between news stories and accurately identify news reprint relations. These results benefit news diffusion pattern analysis and copyright protection.
Multi-Domain Semantic Information and Physical Behavior Modeling of Power Systems and Gas Turbines Expanding the Common Information Model

Due to the rapid increase of intermittent energy resources (IERs), there is a need to have dispatchable production available to ensure secure operation and increase opportunity for energy system flexibility. Gas turbine-based power plants offer flexible operation that is being improved with new technology advancements. Those plants provide, in general, quick start together with significant ramping capability, which can be exploited to balance IERs. Consequently, to understand the potential source of flexibility, better models for gas turbines are required for power system studies and analysis. In this paper, both the required semantic information and physical behavior models of such multi-domain systems are considered. First, UML class diagrams and RDF schemas based on the common information model standards are used to describe the semantic information of the electrical power grid. An extension that exploits the ISO 15926 standard is proposed herein to derive the multi-domain semantics required by integrated electrical power grid with detailed gas turbine dynamic models. Second, the Modelica language is employed to create the equation-based models, which represent the behavior of a multi-domain physical system. A comparative simulation analysis between the power system domain model and the multi-domain model has been performed. Some differences between the turbine dynamics representation of the commonly used GGOV1 standard model and a more detailed gas turbine model are shown.
Improving Recognition of Complex Aerial Scenes Using a Deep Weakly Supervised Learning Paradigm

Categorizing highly complex aerial scenes is quite strenuous due to the presence of detailed information with a large number of distinctive objects. Recognition happens by first deriving a joint relationship within all these distinguishing objects, distilling finally to some meaningful knowledge that is subsequently employed to label the scene. However, something intriguing is whether all this captured information is actually relevant to classify such a complex scene? What if some objects just create uncertainty with respect to the target label, thereby causing ambiguity in the decision-making? In this letter, we investigate these questions and analyze as to which regions in an aerial scene are the most relevant and are inhibiting in determining the image label accurately. However, for such aerial scene classification (ASC) task, employing supervised knowledge of experts to annotate these discriminative regions is quite costly and laborious, especially when the data set is huge. To this end, we propose a deep weakly supervised learning (DWSL) technique. Our classification-trained convolutional neural network learns to identify discriminative region localizations in an aerial scene solely by utilizing image labels. Using the DWSL model, we significantly improve the recognition accuracies of highly complex scenes, thus validating that extra information causes uncertainty in decision-making. Moreover, our DWSL methodology can also be leveraged as a novel tool for concrete visualization of the most informative regions relevant to accurately classify an aerial scene. Finally, our proposed framework yields a state-of-the-art performance on the existing ASC data sets.
Weight Adjusted Naive Bayes

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants. Attribute weighting approaches can be broadly divided into two categories: filters and wrappers. In this paper, we mainly focus on wrapper attribute weighting approaches because they have generally higher classification performance than filter attribute weighting approaches. We propose a weight adjusted naive Bayes approach and simply denote it WANB. In WANB, the importance of each attribute in the classification of a training data set is learned and the weight vector reflecting this importance is updated. We use weight adjustment based on objective functions to find the optimal weight vector. We compare WANB with standard NB and its state-of-the-art attribute weighting approaches. Empirical studies on a collection of 36 benchmark datasets show that the classification performance of WANB significantly outperforms NB and all the existing filter approaches used to compare. Yet at the same time, compared to the existing wrapper approach called DEWANB, WANB is much more efficient and comprehensible.
Prediction of Frequency Nadir by Employing a Neural Network Approach

Ahstract- The increased integration rate of inverter-interfaced devices is affecting the frequency response of the modern power systems. This leads to an increase of the variability of the power generation and to a reduction of the total system's inertia. This evolution of the system necessitates the prediction of frequency metrics, so that the frequency stability of the system can be guaranteed and that necessary mitigation measures can be taken. This paper proposes a method to predict the frequency nadir by using a Neural Network (NN) approach. As the approach uses measurements during a first short time period after the event, it more accurately predicts the frequency nadir compared to using static values. Several inputs for the NN are examined and when the appropriate ones are selected, a highly accurate prediction is accomplished.
Attaché: Towards Ideal Memory Compression by Mitigating Metadata Bandwidth Overheads

Memory systems are becoming bandwidth constrained and data compression is seen as a simple technique to increase their effective bandwidth. However, data compressionrequires accessing Metadata which incurs additional bandwidth overheads. Even after using a Metadata-Cache, the bandwidth overheads of Metadata can reduce the benefits of compression. This paper proposes Attaché, a framework that reduces the overheads of Metadata accesses. The Attaché framework consists of two components. The first component, called the Blended Metadata Engine (BLEM), enables data and its Metadata to be accessed together. BLEM incurs additional Metadata accesses only 0.003% times and removes almost all Metadata bandwidth overheads. The second component, called theCompression Pre-dictor(COPR), predicts if the memory block is compressed. TheCOPR predictor uses a fine-grained line-level predictor, a coarse-grained page-level predictor, and a global indicator. This enables Attaché to predict the compressibility of the memory block before sending a memory read request. We implement Attaché on a memory system that uses Sub-Ranking. On average, Attaché achieves 15.3% speedup (ideal 17%) and saves 22% energy consumption (ideal 23%) when compared to a baseline system that does not employ data compression. Attaché is completely hardware-based and uses only 368KB of SRAM.
Isopod distribution and climate change

Abstract The unique properties of terrestrial isopods regarding responses to limiting factors such as drought and temperature have led to interesting distributional patterns along climatic and other environmental gradients at both species and community level. This paper will focus on the exploration of isopod distributions in evaluating climate change effects on biodiversity at different scales, geographical regions, and environments, in view of isopods’ tolerances to environmental factors, mostly humidity and temperature. Isopod distribution is tightly connected to available habitats and habitat features at a fine spatial scale, even though different species may exhibit a variety of responses to environmental heterogeneity, reflecting the large interspecific variation within the group. Furthermore, isopod distributions show some notable deviations from common global patterns, mainly as a result of their ecological features and evolutionary origins. Responses to human disturbance are not always traceable, but a trend towards community homogenisation is often found under strong global urbanisation processes. In general, even though it is still not clear how predicted climate change will affect isopod distribution, there is evidence that mixed effects are to be expected, depending on the region under study. We still lack robust and extensive analyses of isopod distributions at different scales and at different biomes, as well as applications of distribution models that might help evaluate future trends.
Day-Ahead Rolling Window Optimization of Islanded Microgrid with Uncertainty

The integral variability of electricity demand and intermittency of renewable energy resources (RERs) pose special challenges in the operation of islanded Microgrid (MG). The uncertainty associated with load and generation data further magnifies the problem resulting in huge energy curtailments in real time thus making MG operation expensive. Hence, this paper proposes a stochastic 24-hour ahead rolling window optimal energy scheduling framework to minimize the amount of lost load and lost generation in islanded MG while considering the demand response (DR) potential of heating, ventilation & air-conditioning (HVAC) loads, end user thermal preferences and storage systems. These thermal characteristics are modeled via two-capacity building model. The proposed methodology is formulated as a stochastic linear programming problem. The effectiveness of the framework is validated by simulation for the entire winter season using realistic data. Ultimately, the results are used to calculate and control loss of load probability (LOLP) and loss of load expectation (LOLE) in islanded MG at minimum cost.
EEG Affective Modelling for Dysphoria Understanding

Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress Scale (DASS) is employed to measure dysphoria. Although DASS provides a quantitative assessment of the human affective state, it is subjected to interpretation. To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a more direct method in measuring human affective state by analyzing the brain pattern is proposed. The approach can also address the dynamic affective state which is needed in detecting dysphoria. Brain waves pattern are collected using the electroencephalogram (EEG) device and used as the input to analyze the underlying emotion. In this paper, relevant features were extracted using Mel-frequency cepstral coefficients (MFCC) and classified with Multi-Layer Perceptron (MLP). The experimental results show potential of differentiating between positive and negative emotion with comparable accuracy. Subsequently, it is envisaged that the proposed model can be extended as a tool that can be used to measure stress and anxiety in work places and education institutions.