Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutrophil Gelatinase-associated Lipocalin Significantly Correlates with Ischemic Damage in Patients Undergoing Laparoscopic Partial Nephrectomy

Background: Laparoscopic partial nephrectomy, which minimizes renal function loss due to its nephron sparing nature, has become a standard technique among many experienced centers worldwide for surgical treatment of localized kidney tumors. Although partial nephrectomy will remain the gold standard, we need to improve perioperative management and surgical method to prevent postoperative acute kidney injury. Aims: To demonstrate the frequency of the development of postoperative acute kidney injury following laparoscopic partial nephrectomy in patients with healthy contralateral kidney and determine the early predictive effects of serum neutrophil gelatinase-associated lipocalin on ischemia-reperfusion injury and its association with warm ischemia time. Study Design: Cross-sectional study. Methods: Eighty patients were included. We analyzed tumor size, operating time, duration of anesthesia, and warm ischemia time. Serum samples were obtained for measurement of serum creatinine, estimated glomerular filtration rate, and neutrophil gelatinase-associated lipocalin level preoperatively, at the postoperative 2nd hour, and on postoperative days 1 and 2. We used receiver operating characteristic curve for determining the cut-off point of neutrophil gelatinase-associated lipocalin to detect postoperative acute kidney injury. Correlation analysis was performed using Spearman’s test. Results: Twenty-seven patients developed acute kidney injury on postoperative day 2, and the neutrophil gelatinase-associated lipocalin level increased significantly at the postoperative 2nd hour in the acute kidney injury group (p=0.048). For a cut-off of 129.375 ng/mL neutrophil gelatinase-associated lipocalin, the test showed 70.0% sensitivity and 68.3% specificity for the detection of acute kidney injury at the postoperative 2nd hour. For a cut-off of 184.300 ng/mL neutrophil gelatinase-associated lipocalin, the test exhibited 73.3% sensitivity and 63.3% specificity for the detection of acute kidney injury on postoperative day 1. A significant correlation was found between warm ischemia time and neutrophil gelatinase-associated lipocalin level at the postoperative 2nd hour (r=0.398, p=0.003). The creatinine values were significantly higher and the estimated glomerular filtration rates were significantly lower on postoperative days 1 and 2 in the acute kidney injury group compared with those in the non-acute kidney injury group (p<0.001). Conclusion: The neutrophil gelatinase-associated lipocalin may be used as an alternative biomarker to serum creatinine in differentiation of ischemic damage in patients undergoing laparoscopic partial nephrectomy.
FBG Strain Sensor Applied in Harsh Environment of Aerospace

We have designed a novel FBG strain sensor in this paper, which is optimized for aerospace application and available in harsh environment. The proposed FBG strain sensor is improved in mechanical structure and filler for aerospace, whose characteristic is investigated by FEM simulation and experimental tests. In our design we fix the grating with special mechanical structure and metal coating to ensure the stability. We have demonstrated that developed sensor could detect the minimum strain of 0. 602με by experiment. Our sensor has high sensitivity and good stability and can be used for many aspects in aerospace.
Active fault-tolerant control approach design for rigid spacecraft with multiple actuator faults

In this study, the active fault-tolerant control problem is investigated for a rigid spacecraft in the presence of inertia uncertainty, external disturbance, multiple actuator faults and actuator saturation. The attitude system model of spacecraft and actuator fault model are first given. A sliding mode–based fault detection observer and a radial basis function neural networks–based fault estimation observer are designed to detect the time of actuator fault occurred and estimate the amplitude of unknown fault, respectively. On that basis, an active fault-tolerant control scheme is proposed to accommodate the effects of multiple actuator faults, and it guarantees that the state trajectory of attitude systems without actuator saturation converges to a neighborhood of the origin in finite time. Another active fault-tolerant control scheme is further proposed in actuator saturation constraint case; it ensures that all the closed-loop signals are finite time convergence. Finally, simulation results are given to illustrate the effectiveness of the proposed fault-tolerant control approach.
Classification of Images of Visual Objects Based on Statistical Relevance Measures of Their Structural Descriptions

A problem of classification of visual object images in the space of attributes of descriptors of special points is solved with representation of the description in cluster form and using of statistical measures to calculate relevance of descriptions. The analysis of specific application feature of statistical and metric classifiers in determining the level of relevance of structural descriptions is performed. Comparison of the characteristics of relevance measures on the calculated examples is performed. The Kullback-Leibler divergence is proposed to use as a universal and effective measure for the classification problem. The effectiveness of the proposed approach for application image dataset is shown.
Analysis of Torque and Flux Ripple Factor for DTC and SVM - DTC of Induction Motor Drive

Recently induction motor control techniques have gained their importance in many industrial applications. To obtain fast dynamic torque & flux response Direct Torque Control (DTC) of Induction Motor is implemented using steady state & transient input conditions in MATLAB/Simulink. In order to control both torque and stator flux directly, they need to be predicted to limit them within hysteresis band closed to desired values. This can be realized by selecting suitable sector in space vector modulation. It can perceive that the torque is dependent on the stator flux, rotor flux and the angle between their vectors. In this paper, we have simulated and analysed torque and flux ripple factor for different conditions.
Importance Weighted Evolution Strategies

Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient when compared to competing RL methods. One of the main causes of such inefficiency is the collection of large batches of experience, which are discarded after each policy update. In this work, we study how to perform more than one update per batch of experience by means of Importance Sampling while preserving the scalability of the original method. The proposed method, Importance Weighted Evolution Strategies (IW-ES), shows promising results and is a first step towards designing efficient ES algorithms.
Coverage Centrality Maximization in Undirected Networks

Centrality metrics are among the main tools in social network analysis. Being central for a user of a network leads to several benefits to the user: central users are highly influential and play key roles within the network. Therefore, the optimization problem of increasing the centrality of a network user recently received considerable attention. Given a network and a target user v, the centrality maximization problem consists in creating k new links incident to v in such a way that the centrality of v is maximized, according to some centrality metric. Most of the algorithms proposed in the literature are based on showing that a given centrality metric is monotone and submodular with respect to link addition. However, this property does not hold for several shortest-path based centrality metrics if the links are undirected.In this paper we study the centrality maximization problem in undirected networks for one of the most important shortestpath based centrality measures, the coverage centrality. We provide several hardness and approximation results. We first show that the problem cannot be approximated within a factor greater than 1 − 1/e, unless P = NP, and, under the stronger gap-ETH hypothesis, the problem cannot be approximated within a factor better than 1/no(1), where n is the number of users. We then propose two greedy approximation algorithms, and show that, by suitably combining them, we√ can guarantee an approximation factor of Ω(1/ n). We experimentally compare the solutions provided by our approximation algorithm with optimal solutions computed by means of an exact IP formulation. We show that our algorithm produces solutions that are very close to the optimum.
Control Design for an Articulated Truck With Autonomous Driving in an Electrified Highway

The basis of an electrified highway or eHighway is an intelligent current collector combined with a hybrid drive system, where the eHighway trucks collect power from the overhead cables. The first prototype for these vehicles has been developed by Siemens, but these vehicles need drivers. This paper presents a conceptual model that is an evolution of this approach, proposing a fully electrical vehicle with autonomous driving. This idea has been analyzed through simulations by using a model composed by an articulated truck and its electrical powertrain system, including a control system as well, based the on the model predictive control (MPC), widely used in autonomous vehicles. The vehicle model consists of a 2D model with 10 degrees of freedom, corresponding to the longitudinal and lateral displacements of the tractor, the tractor yaw angle, the semi-trailer yaw angle, and the rotations of the six equivalent wheels. Besides, an electric powertrain system, batteries, and a regenerative brake have been integrated into the model. Several simulation cases have been developed corresponding to an adaptive cruise control, a trajectory tracking, and an overtaking maneuver. The aim of these simulations is also to obtain the minimum size of the battery and its autonomy for an overtaking maneuver, since it is necessary to disconnect the truck from the overhead contact line during this maneuver. The simulations show the viability of this conceptual proposal, obtaining results similar to those expected.
An Optimal Control View of Adversarial Machine Learning

I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data poisoning, and adversarial reward shaping. The view encourages adversarial machine learning researcher to utilize advances in control theory and reinforcement learning.
Continuous Querying over Mobile Mapping Stream

MMS (Mobile Mapping System) or mobile mapping is the process of collecting geospatial data from equipment such as laser scanners, cameras and global positioning system (GPS) typically mounted on a mobile vehicle. The MMS is capable of acquiring precise 3D position data including buildings, road shape, signs/marking, guardrails, characters printed on roads, manholes, and area surrounding the road. The 3D data is generated in the form of continuous data streams as the MMS vehicle moves and is primarily used for the generation of 3D DEMs (Digital Elevation Models), road models, building models, etc. The MMS 3D data stream contains a wealth of information and can be processed / queried continuously to obtain very useful information. For instance, continuously obtaining the total number of trees with certain height planted on road side within n kilometers, continuously getting the number of cars with certain properties for last m hours, etc. This paper presents our work-in-progress on the development of a framework for the real-time processing / continuous query processing over 3D data stream and highlight its importance and usage.
A protective role for microRNA-688 in acute kidney injury

Ischemia-reperfusion (I/R) sets off a devastating cascade of events, leading to cell death and possible organ failure. Treatments to limit I/R-associated damage are lacking, and the pathways that drive injury are poorly understood. In this issue of the JCI, Wei and colleagues identify microRNA-668 (miR-668) as a protective factor in acute kidney injury (AKI). miR-668 was shown to repress mitochondrial fission–associated protein MTP18, thereby inhibiting pathogenic mitochondrial fragmentation. In murine models of I/R-induced AKI, treatment with a miR-668 mimetic reduced mitochondrial fragmentation and improved renal function. Moreover, HIF-1&agr; was shown to be required for miR-688 expression in response to I/R. Importantly, Wei et al. show miR-668 upregulation in a cohort of human patients with AKI. Together, these results identify a HIF-1&agr;/miR-668/MTP18 axis that may have potential as a therapeutic target for AKI.
Dynamic Hand Gesture Recognition Using Computer Vision and Neural Networks

Using hand gestures is one of the most natural ways of interacting with the computer and most importantly correct interpretation of moving hand gestures in real-time has many applications. In this paper, the author has designed and developed a system which can recognize gestures in front of a web camera real time using motion history images (MHI) and feedforward neural networks. Firstly, background from captured frames is removed using Gaussian mixture based background/foreground segmentation algorithm in order to capture moving areas in the frame and thereafter median filtering has applied to remove random noise from the frame. Then binary thresholding with Otsu's binarization has applied and it will identify optimal threshold value and these processed frames are merged and cumulative motion history image is generated using a developed algorithm based on the structural similarity measure. Structural similarity between the cumulated image and the initial frame also calculated and used in this algorithm. Finally feed forward neural network with stochastic gradient-based optimizer has used to classify the gestures.
Ares: A High Performance and Fault-Tolerant Distributed Stream Processing System

Distributed Stream Processing Systems (DSPSs) have been widely deployed to process infinite data streams. Short processing latency and short recovery time are both vital for many DSPS applications. Existing DSPS designs commonly leverage elaborated task allocation strategies to achieve short processing latency. Such designs, however, ignore the requirement of system fault tolerance. Indeed, providing fault tolerant capability in a DSPS can cause significant degradation of system performance. Especially, the intrinsic dependency between upstream and down-stream tasks can incur cascaded waiting during recovery, leading to prohibitively long recovery time. In this paper, we propose Ares, a high performance and fault tolerant DSPS. Ares considers both system performance and fault tolerant capability during task allocation. In the design of Ares, we formalize the problem of Fault Tolerant Scheduler (FTS) for finding an optimal task allocation which maximizes the system utility. We use a game-theoretic approach to solve the FTS problem and propose a novel Nirvana algorithm based on best-response dynamics. We mathematically prove the existence of Nash equilibrium in the FTS game. We implement Ares atop Apache Storm and conduct comprehensive experiments to evaluate this design. The results show that, compared to existing designs Ares achieves a 3.6× improvement of throughput, as well as reducing the processing latency and the recovery time by 50.2% and 52.5%, respectively.
Side Effect Rates of Opioids in Equianalgesic Doses Via Intravenous Patient-Controlled Analgesia: A Systematic Review and Network Meta-analysis.

BACKGROUND Side effects of opioids used for the treatment of acute pain frequently limit their analgesic quality. Many studies have compared opioid side effects in patient-controlled analgesia (PCA), but it remains unclear whether there are specific side effect profiles that can be exploited when choosing an opioid for a patient. In this review, we wanted to determine the risk ratios (RRs) for the most common side effects when using different opioids for intravenous PCA in equianalgesic doses and rank the substances accordingly. METHODS A search of MEDLINE, EMBASE, the Cochrane Library (CENTRAL), and Web of Science identified 63 randomized controlled trials comparing opioids under equianalgesic conditions. Inclusion criteria were comparable pain stimulus between groups, equal coanalgesic treatment, and comparable resulting pain scores. Quality of studies was assessed using the Cochrane risk of bias tool with 6 items. Frequentistic network meta-analysis was conducted with morphine as the comparator. This method not only summarizes all estimated effects from direct comparisons of different interventions but also allows for indirect comparisons between interventions that can be linked via the common comparator, in which case the indirect evidence can be used to enhance the precision of the direct comparisons. Primary end points of this study were RRs for nausea and vomiting, pruritus, and events of sedation, as well as mean differences for scores of sedation. Events of respiratory depression were counted. Secondary end point was patient satisfaction (mean difference). The study protocol was registered at PROSPERO (CRD42017062355). RESULTS Sixteen opioid interventions were compared in the largest network (nausea and vomiting outcome) and 7 opioid interventions in the smallest network (sedation events outcome). Most interventions did not differ from morphine on the primary outcomes (side effects), with some exceptions. Buprenorphine had a significantly higher RR of nausea and vomiting, whereas fentanyl had a lower RR of nausea and vomiting. Nalbuphine, butorphanol, methadone, and pethidine/meperidine had a lower risk of pruritus. Respiratory depression was rare (22 of 2452 patients). Pethidine/meperidine, fentanyl, and oxymorphone caused significantly lower sedation scores. Tramadol caused significantly lower satisfaction scores, whereas oxycodone, alfentanil, remifentanil, fentanyl, and pethidine/meperidine caused significantly higher satisfaction scores. CONCLUSIONS The opiate chosen for treatment most likely has little effect on the incidence of pruritus and nausea/vomiting, although considerable differences exist in terms of better and worse opioids in the presented rankings. Larger differences between drugs were observed with regard to sedation and patient satisfaction, and choosing the appropriate opioid may help to improve PCA in this regard.
Forecasting Results of Sport Events Through Deep Learning

The importance of competitive sport events such as the World Cup and the World Baseball Classic for a majority of people can be easily found through the heated discussions in newspapers and other types of media such as the Internet while the fad hits. They are also highly discussed topics. Many people are even one-day fans with the same expectations; that is, they want their team to win. It is, however, very difficult to determine which team will stand out among the many. In this study, records and data from the many contests that the National Basketball Association (NBA), which also deals with competitive sports, has held will be analyzed and discussed in order to forecast results of games. The deep learning approach will be adopted and convolutional neural networks and data from 4147 games over the past 3 years will be used for analysis and to facilitate training on and forecasts done applying the model. Finally, forecasting results will be discussed. In previous studies, convolutional neural networks were more frequently applied to identifying images or objects. Therefore, with the current study, the hope is to combine deep learning in the forecast of event results and that the approach helps add to the accuracy of forecast results compared to other classifiers.
Implementation of Fuzzy-Sliding Mode Controller (FSMC) for a Grid Connected Solar Photovoltaic System to Control Voltage

This paper presents a method for operation of a solar photovoltaic system connected to a Grid, built on the combination of Fuzzy controller and sliding mode scheme. The hybrid fuzzy sliding mode controller (FSMC) regulate the voltage and frequency of the solar photovoltaic connected to grid. The proposed robust Controller shares the real and reactive power that helps to control the voltage and frequency of the system. The proposed controller is robust, simple in design, rule based, heuristic, prone to external disturbances, system uncertainties and eliminates the chattering phenomena. Simulation results have been carried out to present the performance of the Control Scheme.
Statistical Analysis of Surface Temperature Distribution Pattern in Plantar Foot of Healthy and Diabetic Subjects Using Thermography

Diabetic foot complications are a major cause of concern for diabetic patients as it affects mobility and quality of life. Any computer aided diagnosis system would be very useful in the early detection and hence treatment and cure. For such a system, the surface temperature distribution patterns in the plantar region of the foot of both healthy and diabetic subjects have to be analyzed to detect any abnormality. In this paper we have analyzed the infrared thermal images of 62 diabetic and 20 healthy subjects to identify the temperature distribution patterns capable of detecting diabetic foot complications. The images were taken using Fluke TiX560 thermal imager. Image processing and analysis was done in MATLAB.
Availability of Web Services based on Autonomous Classification and Negotiation of SLAs

Establishing and ensuring the QoS (quality of service) of a component like a Web service is a critical issue, this must allow establishing a trust relationship between the service provider and a client waiting for certain reliability. However, unlike well-established specifications in the functional domain of Web services (such as WSDL, SOAP, or UDDI), there are no officially recognized standards for the description and establishment of QoS properties. However, a number of works aim to provide answers to this shortcoming through Service Level Agreements (SLAs), the goal of which is to enable an agreement between consumers and service providers to be reached on the level Of QoS that the service must provide.The solution proposed in this article allow us to resolve in one hand the problem of manual management, and in the other hand the problem of congestion, in other words the time of response will decrease. Moreover, it our principal purpose is to guarantee high availability of services by the abstraction of the provider of service. Moreover the goal is to improve availability and reducing response time by classification of SLAs using k-means algorithm.
Physical activity and epigenetic biomarkers in maternal blood during pregnancy.

AIM Investigate associations of leisure time physical activity (LTPA) with DNA methylation and miRNAs during pregnancy. Patients & methods: LTPA, candidate DNA methylation and circulating miRNAs were measured (average 15 weeks gestation) in pregnant women (n = 92). RESULTS Each additional hour of prepregnancy LTPA duration was associated with hypermethylation in C1orf212 (β = 0.137, 95% CI: 0.004-0.270) and higher circulating miR-146b-5p (β = 0.084, 95% CI: 0.017-0.151). Each additional metabolic equivalent hour of early-pregnancy LTPA energy expenditure was associated with higher circulating miR-21-3p (β = 0.431, 95% CI: 0.089-0.772) in women carrying female offspring, and lower circulating miR-146b-5p (β = -0.285, 95% CI: -0.528 to -0.043) and miR-517-5p (β = -0.406, 95% CI: -0.736 to -0.076) in women carrying male offspring. CONCLUSION Our findings suggest that LTPA may influence maternal epigenetic biomarkers, possibly in an offspring sex-specific manner.