Miracle-dz/UniFAR-FTUs huggingface dataset:
An Active Multiplier-by-Six S-MMIC for 500 GHz

A frequency multiplier-by-six based on a 35 nm gate-length metamorphic HEMT technology is presented. The S-MMIC offers an average output power of −15 dBm in the frequency range of 480 to 506 GHz and a maximum output power of −9 dBm at 506 GHz without additional amplification stages at an input power of 5 dBm. A spurious suppression of 35 dBc at 500 GHz was achieved.
Meal Detection for Type 1 Diabetes Using Moving Horizon Estimation

In this paper, we develop a method for detection of unannounced meals for blood glucose regulation in diabetes. A smoothing formulation using moving horizon estimation (MHE) estimates the unknown rate (g/min) of carbohydrate (CHO) ingestion. The inputs to the meal detection algorithm are the CGM measurements and insulin infusion rate. The MHE uses second-order linear input-output models for insulin to subcutaneous (sc) glucose dynamics and for the carbohydrate (CHO) to sc glucose dynamics. We test the algorithm on 9 in silico type 1 diabetes patients and a total of 45 meals during 13.5 days of simulation. The model in the patient simulator is a nonlinear model of glucose regulation. Results indicate that the detection delay is 33 min, and the algorithm has two false negatives (96 % sensitivity) and one false positive. The mean elevation in sc glucose concentration due to meals is 10.6 mg/dL at the detection time.
Automated Table Partitioner (ATAP) in Apache Hive

Big Data and Predictive Analytics have been a game-changing paradigm in academia and industry for the past decade, inspiring numerous efforts in multiple spaces. One of many such technologies is Hadoop, an open-sourced framework based on MapReduce for highly distributive and scalable solutions. As Hadoop became more popular, other technologies were built, making it an ecosystem by itself. Currently, there are hundreds of tools and utilities that add-on to the Hadoop framework, and Apache Hive is one of the most prominent options. Hive is built as a data warehousing layer that interacts with Hadoop and the underlying filesystem, HDFS. It quickly became the market leader in query processing as it provides better user experience than MapReduce. Nevertheless, it imposes rigid structures that are unyielding to the ever changing nature of data. This paper proposes a novel mean of automating the table partitioning in Hive. It includes a lexical analyzer that reads HiveQL queries and, in return, issues Data Definition Language (DDL) for table restructure if a particular column is read more than the user-set coefficient factor. Multiple experiment made for this research have returned results that further solidified this proof of concept for its feasibility, adaptability and usability.
Wearable EMG Shirt for Upper Limb Training

This work presents a wearable shirt fitted with dry electrodes to measure EMG in upper limbs during physical exercise. The hardware acquires signals from 6 channels and transmits wirelessly to a PC via Bluetooth. In the PC, EMG signals are analyzed to give feedback to the user regarding the exercise being performed. To alert for muscle fatigue, the Dimitrov index is used, with much better results that classic mean or median EMG frequency tracking. The system is able to monitor EMG activity and provide valuable information in real–time for professional and amateur athletes and their sports coach, improving their training protocols.
Movement Economy in Soccer: Current Data and Limitations

Soccer is an intermittent team-sport, where performance is determined by a myriad of psychological, technical, tactical, and physical factors. Among the physical factors, endurance appears to play a key role into counteracting the fatigue-related reduction in running performance observed during soccer matches. One physiological determinant of endurance is movement economy, which represents the aerobic energy cost to exercise at a given submaximal velocity. While the role of movement economy has been extensively examined in endurance athletes, it has received little attention in soccer players, but may be an important factor, given the prolonged demands of match play. For this reason, the current review discusses the nature, impact, and trainability of movement economy specific to soccer players. A summary of current knowledge and limitations of movement economy in soccer is provided, with an insight into future research directions, to make this important parameter more valuable when assessing and training soccer players’ running performance.
Refolding Proteins from Inclusion Bodies using Differential Scanning Fluorimetry Guided (DGR) Protein Refolding and MeltTraceur Web

Differential Scanning Fluorimetry Guided Refolding (DGR) is a simple methodology that can be used to rapidly screen for and identify conditions capable of accurately refolding protein preparations, such as those obtained from Escherichia coli inclusion bodies. It allows for the production in E. coli of functional proteins that would otherwise require far more expensive production methods. This unit describes how to set up a DGR refolding assay, perform DGR refolding trials in microplate format, use MeltTraceur Web software to interactively analyze the resulting data, scale‐up protein production via refolding, and lastly, validate that the protein is properly folded. © 2018 by John Wiley & Sons, Inc.
Diabetic Cardiomyopathy: Current and Future Therapies. Beyond Glycemic Control

Diabetes mellitus and the associated complications represent a global burden on human health and economics. Cardiovascular diseases are the leading cause of death in diabetic patients, who have a 2–5 times higher risk of developing heart failure than age-matched non-diabetic patients, independent of other comorbidities. Diabetic cardiomyopathy is defined as the presence of abnormal cardiac structure and performance in the absence of other cardiac risk factors, such coronary artery disease, hypertension, and significant valvular disease. Hyperglycemia, hyperinsulinemia, and insulin resistance mediate the pathological remodeling of the heart, characterized by left ventricle concentric hypertrophy and perivascular and interstitial fibrosis leading to diastolic dysfunction. A change in the metabolic status, impaired calcium homeostasis and energy production, increased inflammation and oxidative stress, as well as an accumulation of advanced glycation end products are among the mechanisms implicated in the pathogenesis of diabetic cardiomyopathy. Despite a growing interest in the pathophysiology of diabetic cardiomyopathy, there are no specific guidelines for diagnosing patients or structuring a treatment strategy in clinical practice. Anti-hyperglycemic drugs are crucial in the management of diabetes by effectively reducing microvascular complications, preventing renal failure, retinopathy, and nerve damage. Interestingly, several drugs currently in use can improve cardiac health beyond their ability to control glycemia. GLP-1 receptor agonists and sodium-glucose co-transporter 2 inhibitors have been shown to have a beneficial effect on the cardiovascular system through a direct effect on myocardium, beyond their ability to lower blood glucose levels. In recent years, great improvements have been made toward the possibility of modulating the expression of specific cardiac genes or non-coding RNAs in vivo for therapeutic purpose, opening up the possibility to regulate the expression of key players in the development/progression of diabetic cardiomyopathy. This review summarizes the pathogenesis of diabetic cardiomyopathy, with particular focus on structural and molecular abnormalities occurring during its progression, as well as both current and potential future therapies.
Device for Light-Matter Interaction Enhancement in the Full THz Range for Precise Spectroscopy of Small Volume Samples

We designed, fabricated, characterized and performed the first experimental uses of a device assembling a waveguide and two antennas. The goal of our device is to confine the broadband pulse of a time-domain spectroscopy setup in the slot of the waveguide with the minimum losses and dispersion and with the thinnest slot. With this device, we aimed at analysis a bio sample of the volume below 200 μ1.
Analyzing the Components of Emotional Competence of Football Coaches: A Qualitative Study from the Coaches’ Perspective

Emotional Competence (EC) is regarded as a fundamental skill for sports coaches. However, the applications of EC in football coaching are not well understood. This study analyzed the specific emotional processes football coaches experience. We interviewed 18 football coaches and analyzed the interview transcripts by using a systematic analysis process based on Grounded Theory principles. We derived a model from this analysis that comprises a four-phase process: emotional triggers, emotional experiences, emotion regulation strategies, and emotional consequences. In this model, we identified four categories which act as triggers of emotions in football coaches. These emotions can be positive or negative and are manifested at three levels. However, the coaches vary in their capability to perceive emotions. Our model also shows that coaches’ emotion regulation strategies influence the effect of emotional experiences. Experienced emotions promote consequences with psychological and social implications for coaches and may influence their perception of future situations. In short, the process seems to be circular. This finding suggests that the ability to deal with emotions is an important aspect for football coaches.
Self-Assisted Standing Enabled by Non-Invasive Spinal Stimulation after Spinal Cord Injury.

Neuromodulation of spinal networks can improve motor control after spinal cord injury (SCI). The objectives of this study were to (1) determine whether individuals with chronic paralysis can stand with the aid of non-invasive electrical spinal stimulation with their knees and hips extended without trainer assistance, and (2) investigate whether postural control can be further improved following repeated sessions of stand training. Using a double-blind, balanced, within-subject cross-over, and sham-controlled study design, 15 individuals with SCI of various severity received transcutaneous electrical spinal stimulation to regain self-assisted standing. The primary outcomes included qualitative comparison of need of external assistance for knee and hip extension provided by trainers during standing without and in the presence of stimulation in the same participants, as well as quantitative measures, such as the level of knee assistance and amount of time spent standing without trainer assistance. None of the participants could stand unassisted without stimulation or in the presence of sham stimulation. With stimulation all participants could maintain upright standing with minimum and some (n = 7) without external assistance applied to the knees or hips, using their hands for upper body balance as needed. Quality of balance control was practice-dependent, and improved with subsequent training. During self-initiated body-weight displacements in standing enabled by spinal stimulation, high levels of leg muscle activity emerged, and depended on the amount of muscle loading. Our findings indicate that the lumbosacral spinal networks can be modulated transcutaneously using electrical spinal stimulation to facilitate self-assisted standing after chronic motor and sensory complete paralysis.
Training Frankenstein's Creature to Stack: HyperTree Architecture Search

We propose HyperTrees for the low cost automatic design of multiple-input neural network models. Much like how Dr. Frankenstein's creature was assembled from pieces before he came to life in the eponymous book, HyperTrees combine parts of other architectures to optimize for a new problem domain. We compare HyperTrees to rENAS, our extension of Efficient Neural Architecture Search (ENAS). To evaluate these architectures we introduce the CoSTAR Block Stacking Dataset for the benchmarking of neural network models. We utilize 5.1 cm colored blocks and introduce complexity with a stacking task, a bin providing wall obstacles, dramatic lighting variation, and object ambiguity in the depth space. We demonstrate HyperTrees and rENAS on this dataset by predicting full 3D poses semantically for the purpose of grasping and placing specific objects. Inputs to the network include RGB images, the current gripper pose, and the action to take. Predictions with our best model are accurate to within 30 degrees 90% of the time, 4 cm 72% of the time, and have an average test error of 3.3 cm and 12.6 degrees. The dataset contains more than 10,000 stacking attempts and 1 million frames of real data. Code and dataset instructions are available at github.com/jhu-lcsr/costar_plan .
An Accelerated Algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis

As a non-contact life detecting equipment of smart campus, the ultra-wide band (UWB) radar life detector is widely used in finding victims of post-disaster search and rescue. However, due to the complex detection environment and the weak life characteristics of victims, the detecting time consuming by current detecting methods is long meanwhile the sensitivity is low, as a result, the process of search and rescue is slow and the probability of missed detection is high. To solve this problem, three works have been done in this research: first, a mathematical model of the echo of UWB radar is established; second, a concept, the critical speed of fast and slow objects, is put forward as a reference value of signal processing; and third, a new accelerated algorithm of which utilizes block data analysis to detect micro-moving objects is proposed in this paper. It has been verified both by simulation and experiment that the new algorithm can improve the detecting sensitivity and the speed of detection.
Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights

Colorectal cancer is one of the common cancers in the United States. Polyps are one of the major causes of colonic cancer, and early detection of polyps will increase the chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method using Asu Mayo Test clinic database, which contains colonoscopy videos of different patients. Our proposed method reaches a dice score of 71.20% and accuracy of more than 90% using the mentioned dataset.
Fault Analysis of Aging McKibben Pneumatic Artificial Muscle in Terms of its Model Parameters

The study analyzes a fault of McKibben Pneumatic Artificial Muscles (PAMs) focusing on unknown parameters used in nonlinear models of the PAMs. In the analysis, a change in dynamics, such as the steady-state responses to increasing the number of contraction movements, was measured through experiments and was subsequently associated with a change in model parameters. Subsequently, the fault state, which involves making a hole in the rubber tube of the PAM, and the normal state were classified by using a support vector machine (SVM) based on the change in model parameters. Furthermore, a few model parameters that can characterize the state of the PAM were extracted in terms of generalization performance. The results can be applied to other systems, such as hydraulic systems of a construction machine, and used to improve control performance by designing control systems to alleviate the influence of changing dynamics.
3MRP+: An Improved Multimetric Geographical Routing Protocol for VANETs

The design of routing protocols in vehicular ad hoc networks (VANETs) is fundamental to achieve a high packets' delivery ratio. Routing protocols whose operation considers the nodes' positions use updated routing information according to the reception frequency of hello messages. The routing information can help to improve the routing operation by including several metrics such as vehicle's trajectory, vehicle's density, percentage of packet losses, among others. In addition, there is a trade-off between the beaconing frequency and the overhead injected to the network: a high frequency provides a better accuracy on selecting the best forwarding node to route a packet but at the same time the overhead is increased. However, a low frequency will provide less accuracy on selecting the best forwarding candidate node but the overhead injected to the network will be decreased. In this paper, we have designed an efficient method to improve the accuracy of the nodes' position used to select the next forwarding node without any modification on the beaconing frequency value. Our approach improves the operation of the routing protocol used in our performance evaluation. Simulations show the benefits of our proposal, increasing the accuracy of the nodes' selection and maintaining the same level of overhead, without the necessity to increase the beacon frequency.
Space Program Scheme for 3-D NAND Flash Memory Specialized for the TLC Design

A new space program (PGM) scheme is proposed to achieve reliable triple-level-cell (TLC) 3-D NAND flash memory. Considering the lateral diffusion issue of stored electrons in the nitride storage layer, the proposed scheme stores electrons in the nitride layer of the space region between adjacent cells to suppress the lateral movement of trapped electrons in the programmed target cells. The effect of the space PGM can be sustained until 104 s at 90 °C and up to 1k read cycles at 25 °C. The programmed space region of the nitride layer improves the retention characteristics of the cells in the PGM state by 40% and remarkably reduces the Vth redistribution.
A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI

Delineation of lung tumor from adjacent tissue from a series of magnetic resonance images (MRI) poses many difficulties due to the image similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is essential in planning a radiation therapy to prevent healthy tissues from receiving excessive radiation. The manual delineation of the entire MRI sequence is tedious, time-consuming and costly. This study investigates how one can perform automatic tracking of tumor boundaries during radiation therapy using convolutional neural networks. We proposed to use a convolutional neural network architecture with modified Dice metric as the cost function. The proposed approach was evaluated over 600 images in comparison to expert manual contours. The proposed method yielded an average Dice score of $0.91 \pm 0.03$ and Hausdorff distance of $2.88 \pm 0.86$ mm. The proposed approach outperformed recent state-of-the-art methods in terms of accuracy in the delineation of the mobile tumors.
Effect of mesenchymal stem cells‐derived exosomes on tumor microenvironment: Tumor progression versus tumor suppression

Mesenchymal stem cells (MSCs) are multipotent cells with the potential to differentiate into different cell types. Owing to their immunosuppressive and anti‐inflammatory properties, they are widely used in regenerative medicine, but they have a dual effect on cancer progression and exert both growth‐stimulatory or ‐inhibitory effects on different cancer types. It has been proposed that these controversial effects of MSC in tumor microenvironment (TME) are mediated by their polarization to proinflammatory or anti‐inflammatory phenotype. In addition, they can polarize the immune system cells that in turn influence tumor progression. One of the mechanisms involved in the TME communications is extracellular vesicles (EVs). MSCs, as one of cell populations in TME, produce a large amount of EVs that can influence tumor development. Similar to MSC, MSC‐EVs can exert both anti‐ or protumorigenic effects. In the current study, we will investigate the current knowledge related to MSC role in cancer progression with a focus on the MSC‐EV content in limiting tumor growth, angiogenesis, and metastasis. We suppose MSC‐EVs can be used as safe vehicles for delivering antitumor agents to TME.
Dexamethasone inhibits NF-кBp65 and HMGB1 expression in the pancreas of rats with severe acute pancreatitis

Severe acute pancreatitis (SAP) starts as a local inflammation of pancreatic tissue that induces the development of multiple extra-pancreatic organ dysfunction; however, the underlying mechanisms remain unclear. The present study was designed to evaluate the effect of dexamethasone (DXM) on pancreatic damage and to investigate the role of high-mobility group box-1 (HMGB1) and nuclear factor-κB (NF-κBp65) in the development of SAP in animal and cell models. For the in vivo experiment, 35 Sprague-Dawley rats were randomly assigned to three groups: The sham-operation control group, the SAP group and the DXM treatment group. Histological analysis revealed that, when DXM was infused into SAP rats, edema formation and structural alterations with necrosis were reduced, and the number of apoptotic cells was markedly reduced. In addition, compared with the SAP group, the expression level of HMGB1 was significantly decreased in the nucleus and the expression level of NF-κBp65 was significantly decreased in the cytoplasm from rats treated with DXM. In vitro, DXM was able to suppress the apoptosis and cell death induced by caerulein (CAE), and DXM could suppress the expression of NF-κBp65 and HMGB1 induced by CAE, as demonstrated by western blotting and immunofluorescence analysis. Therefore, these results provide an experimental basis for investigating the underlying therapeutic mechanisms of DXM treatment for SAP.
MDROntology : An Ontology for Managing Ontology Changes Impacts on Business Rules

In this paper, we focus on the impact of ontology changes on rules, or more specifically on business rules authored over ontologies. For this we develop the MDR framework (Model-Detect-Repair) based on the MDROntology and a set of rules. As rules depend on the ontology entities, ontology changes may make them inconsistent. The MDR framework analyses an ontology change to detect its impact (e.g. inconsistency) on rules and propose solutions, called repair, to repair the change impact. The MDROntology includes, among others, a change model, ontology changes classification and the impact of such changes on rules.