Miracle-dz/UniFAR-FTUs huggingface dataset:
Postoperative observational study of sublingual microcirculation after Caesarean sections with Sidestream Dark-field imaging technique

Background: The ultimate effects of resuscitation in patients with hemodynamic instability are assumed to be in the microcirculation. By monitoring sublingual microcirculation using Sidesteam dark-field imaging (SDF), microcirculatory function can be evaluated. Sublingual microcirculatory dysfunction identified with SDF is associated with increased risk of morbidity and mortality in multiple clinical scenarios. Previous studies suggest microvascular alterations in a number of pathological conditions, but still more information about microcirculatory alterations in different pathologies is needed. Aim/Hypothesis: The aim of our study was to investigate the prevalence of sublingual microcirculatory alterations in Post-OP Caesarean sections, Post-OP liver resections and a heterogeneous ICU population using the SDF-imaging with AVA4 software. A secondary objective was to evaluate the AVA4 software’s effectiveness in researching the microcirculation. Methods: Data on patient diagnosis, type of surgery, systemic hemodynamics variables and laboratory values were collected together with simultaneous SDF imaging of the sublingual microcirculation, supplemented with retrospective acquisition of data from medical records. Data analysis was made using Kruskal-Wallis test and Linear regression. Results: 22 patients were included in this study. No group proved any significant difference from the control group of healthy volunteers in any of the chosen microcirculatory parameters. There were no linear relations between the microvascular parameters and blood-lactate levels (all patients) or blood-lactate levels (liver resections). Conclusion: Due to small sample size this study was unable to prove any significant differences between microvascular parameters between patient groups compared to healthy volunteers. To achieve more definite results a larger study population is needed.
Hierarchical optimal dispatching strategy for microgrid system considering user-side resources

In order to take into account both the user-side and generation-side of the microgrid system effectively, this paper focuses on the energy management and optimization of microgrid, establishes an optimal operation model of distributed energy system considering demand-side, and proposes the optimal dispatching strategy of microgrid energy hierachical management. The user-layer adjusts the power consumption according to the predicted power of the new energy micro-source, and then outputs the adjusted load curve. The minimal cost of operation and maintenance and the lowest environmental cost are chosen as the objective functions of the micro-source layer, and then the optimal model of microgrid is established by appropriate scheduling strategy. Finally, the optimization results are evaluated according to the performance evaluation index of microgrid. The simulation results show that the two-way energy interaction between the microgrid and the main grid can reduce the operating cost of the microgrid and the user participation can effectively improve the economic operation of microgrid.
Indoor positioning system simulation for a robot using radio frequency identification

Robotic automation is considered as the fourth industrial revolution by multiple industrialists. Robots are faster, more accurate, less error-prone and cost-effective when compared to human beings. For big companies, it is estimated that by 2025, labor cost savings from the adoption of advanced industrial robots would surpass 20%. However, for small and medium companies, industrial robots are still expensive and unaffordable. Fortunately, with the advent of Internet of Things (IoT), artificial intelligence, open source hardware and software, and the declining price of electronic components, a new generation of smaller, cheaper and friendlier robots are emerging, for small and medium businesses. In this paper, we propose the simulation of an Indoor Positioning System (IPS) robot, which performs a forklift operation in a warehouse. The system uses Radio Frequency Identification (RFID) technology, and Java programming language for exchanging data from a server. An ESP8266 NodeMCU module, an Arduino microcontroller and various components such as MFRC522 to read RFID tags, Ultrasonic Sensors HC-SR04 for detecting obstacles are used. This resulted in a very low cost, reliable and multifunctional system. Simulations have demonstrated that the mobile robot can transmit packets over the network to a local server for mapping without losses. Moreover, it can receive packets from the server to obtain destination points and mapping obstacles while moving around the RFID tagged floor.
Algoritmos de Correção de Outliers para Curvas de Potência utilizando inteligência artificial

One of the main problems of the data acquired by power utilities is the presence of outliers affecting the database measurements in the electrical system, damaging the analyzes of the distribution scenario. This work proposes a new module to complement the measurements made by the utilities. Two algorithms for outliers correction were developed using artificial intelligence techniques: fuzzy logic and artificial neural networks. The first technique, with a fuzzy approach, develops an inference system based on the variations of previous measurements to determine future variation. In the second algorithm developed using NN, the outliers were filled using a prediction model using 10 previous samples. To demonstrate the applicability of the developed methods, a case study is performed on a substation in a city of Paraíba.
Stuttgart Fachbereich Mathematik Optimal Grading of the Newest Vertex Bisection and H 1-Stability of the L 2-Projection

We show for adaptive triangulations in 2d, which are generated by the Newest Vertex Bisection, an optimal grading estimate. Roughly speaking, we construct from the piecewise constant mesh-size function a regularized one with the following two properties. First, the two functions are equivalent, and second, the regularized mesh-size function differs at most by a factor of 2 on neighboring elements. In combination with [1] this optimal grading estimate enables us to show that the L2-orthogonal projections onto the space of continuous Lagrange finite elements up to order twelve is H1-stable. We extend these results to a modified Red-Green-Refinement.
Design of a control system for an intelligent building

This paper discusses about intelligent buildings and design of our own simple control system that will communicate with multiple actuators and sensors over Ethernet and to evaluate the measured values. The system consist of three devices based on Arduino Ethernet development boards and Quido ETH 4/4 board. This system can be used to control processes in intelligent buildings.
A 1.25-GS/s 7-b SAR ADC With 36.4-dB SNDR at 5 GHz Using Switch-Bootstrapping, USPC DAC and Triple-Tail Comparator in 28-nm CMOS

This paper presents a 1.25-GS/s 7-b single-channel successive approximation register (SAR) analog-to-digital converter (ADC) that achieves a low input frequency SNDR/SFDR of 41.4/51 dB, while the SNDR/SFDR at Nyquist is 40.1/52 dB and remains still 36.4/50.1 dB at a 5-GHz input frequency (eighth Nyquist zone) without any calibration. The high and nearly constant linearity is enabled by an improved bootstrap circuit for the input switch, while the high sampling rate, the highest among recently published >34-dB SNDR single-channel SAR ADCs, is accomplished by a triple-tail dynamic comparator and a unit-switch-plus-cap (USPC) capacitive digital-to-analog converter (CDAC). To further enhance the ADC speed, the SAR logic operates in parallel to the comparator, eliminating its timing from the critical loop. The prototype chip in 28-nm bulk CMOS occupies a core area of 0.0071 mm2 and consumes 3.56 mW from a 1-V supply, leading to a Walden figure-of-merit of 34.4 fJ/conversion-step at Nyquist.
A convolution-based approach for fixed-pattern noise removal in OCR

There is some fixed-pattern noise in the OCR text image and the denosing is needed to improve the accuracy of recognition. In this paper, a convolution-based approach for the fixed-pattern noise removal in OCR is proposed. The approach identifies the location of text content pixels and removes noise pixels based on the convolution kernel. The experiment shows that the approach is an effective way to remove the underline and improve the accuracy of recognition. For its generality, the algorithm is also applicable of removing other type of fixed-pattern noise.
MatLab-OpenDSS co-simulation environment: An alternative tool to investigate DSG connection

This work presents a hybrid simulation tool that combines quasi-static and electromagnetic transients models for faster and accurate results. The electrical network is splited in two different areas and they are solved by OpenDSS and Sim-PowerSys/Matlab programs, respectively. During the simulation, an Intranet Server controls the data transaction between the two simulation environments by using a TCP/IP protocol. The proposed methodology is demonstrated by integrating three PV power plant into different points of a distribution feeder based on 13-bus IEEE benchmark. Simulation results are used to validate the accuracy of the proposed method and evaluate the time reduction of the hybrid simulator when it is compared to the time when the same electrical system is fully simulated in the transient electromagnetic program.
Automatic target recognition in SAR images: Comparison between pre-trained CNNs in a tranfer learning based approach

Synthetic aperture radar (SAR) are high resolution imaging radar systems. In many SAR applications classifying objects that are detected within the SAR image is important. In this paper an approach is proposed to tackle the Synthetic SAR Automatic Target Recognition (ATR) problem. The proposed scheme is based on a transfer leaning approach where three different pre-trained Convolutional Neural Networks (CNNs) are used as feature extractors in combination with a Support Vector Machine classifier (SVM). The CNNs used in this paper are AlexNet, VGG16 and GoogLeNet. The performance of these three CNNs is compared in regards to the SAR-ATR problem; where it is observed that AlexNet gives the best performance accuracy of 99.27%.
Analysis and test of sound delay on Web Audio under different situations

With the progress and development of science and technology, the sound is applied in different fields. However, people will inevitably encounter the sound delay due to different factors while they are researching on the sound triggered by the event, which has influence on the expected effect to some extent.so It is an imminent thing to propose a reasonable test of sound to delay and analyze the factors causing the sound delay to optimize it. Based on Web Audio, this paper aims to propose a reasonable and effective test method by analyzing the delay generated by audio on different platforms, so as to realize the purpose of automatically testing the sound delay by setting the fixed length. The experimental results show that the delay analyzed by the external stress test is different from that analyzed by the internal stress test under different browsers and different operating systems.
Stepbox: A proposal of share economy transport service

Transportation activity is essential for much of the economy, since most economic activities depend on the movement of goods and people. Currently Logistics has been hampered by changes in external and internal environments, such as: delivery time, tracking and fleet management, customer opinion, supplier dependency, economic crisis. Considering the problems reported, as well as new market trends, new business models and technologies, Stepbox was developed. A logistics application of goods deliveries that offers services through shared economy. One of the proposals of the Stepbox is to serve as a channel of communication between the transport service applicants and the companies that provide the transport service. In order to provide innovation in this area, Stepbox offers several value propositions for both the freight forwarder and the companies that provide this type of service. For example: shared economy, smart routes, cargo tracking, delivery value suggestion, check-in, qualification of the product collection and delivery process(gamification), geo opportunity. Finally, the evaluation of the Stepbox proposal was carried out, together with the companies and possible clients of the freight transport service. Based on the results, it was identified that there is a strong indication that the new business model proposed by Stepbox suits the reality of companies operating in the logistics sector and solves the problems described in this paper. This clue was also confirmed by the Startup competitions that Stepbox participated in around Brazil and Latin America.
An Adaptive Mechanism for Designing Efficient Snoop Filters

A common mechanism to ensure cache coherence is to issue snoop requests to all processors to check for the presence of cached data. Since most of snoop requests result in misses in caches and waste a lot of power, snoop filters are widely used to filter out unnecessary snoop requests to reduce power consumption. However, snoop filters also suffer from the similar problem that the false positive predictions consume a large amount of power. Substantially, designing an efficient snoop filter has to make tradeoff decisions between the filter rate and hardware cost. Traditionally, the snoop filter rate can be improved by increasing the memory capacity of snoop filters, but results in the burden of hardware overhead. In this paper, we propose an efficient adaptive mechanism to improve the filter rate of snoop filters by duplicating multiple copies of small snoop filters and distributing cache tags evenly to the duplicated copies according to the analytics of page tables. Experimental results show that the adaptive mechanism applied to JETTY snoop filters achieves an average of 19.17% and 76.1% improvement in the filter rate and memory reduction for the Splash 2 benchmarks, respectively.
WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals

Gesture tracking is a basic Human-Computer Interaction mechanism to control devices such as electronic Internet of Things and VR/AR devices. However, prior WiFi signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus cannot be applied for highprecision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from WiFi signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman filter based on Continuous Wiener Process Acceleration model to further filter out tracking noise. Our results show that WiTrace achieves the estimated accuracy of 3.91 cm for initial position on average, and achieves cm-level accuracy, with mean tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.
Voluntary audit and earnings management: The case of small Portuguese companies

In some small Portuguese companies the audit is required while in others companies the audit is voluntary, and our goal is to verify if the voluntary adoption of audit improves the earnings quality. Starting from a sample of small Portuguese companies for a period from 2007 up to 2014 we analyze if the voluntary adoption of audit reduces the earnings management and therefore increases the earnings quality. The small companies can manipulate the earnings for example for reducing the income taxes to pay or to finance as also reducing the interest rate of those finance. The small companies are an important part of businesses, either in terms of employees and number. The voluntary audit is not very studied subject. The results suggest that the voluntary adoption of audit do not reduce the earnings management, and thus do not increase the earnings quality. These results may be due to the fact these companies are not visible and therefore the audit firms could not allocate as much resources to the audit.
Novel Brain Complexity Measures Based on Information Theory

Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain. In this paper, we propose a new set of measures based on information theory. Our approach interprets the brain network as a stochastic process where impulses are modeled as a random walk on the graph nodes. This new interpretation provides a solid theoretical framework from which several global and local measures are derived. Global measures provide quantitative values for the whole brain network characterization and include entropy, mutual information, and erasure mutual information. The latter is a new measure based on mutual information and erasure entropy. On the other hand, local measures are based on different decompositions of the global measures and provide different properties of the nodes. Local measures include entropic surprise, mutual surprise, mutual predictability, and erasure surprise. The proposed approach is evaluated using synthetic model networks and structural and functional human networks at different scales. Results demonstrate that the global measures can characterize new properties of the topology of a brain network and, in addition, for a given number of nodes, an optimal number of edges is found for small-world networks. Local measures show different properties of the nodes such as the uncertainty associated to the node, or the uniqueness of the path that the node belongs. Finally, the consistency of the results across healthy subjects demonstrates the robustness of the proposed measures.
On the Structural and Strong Structural Controllability of Undirected Networks

Characterization of network controllability through its topology has recently gained a lot of attention in the systems and control community. Using the notion of balancing sets, in this note, such a network-centric approach for the controllability of certain families of undirected networks is investigated. Moreover, by introducing the notion of a generalized zero forcing set, the structural controllability of undirected networks is discussed; in this direction, lower bounds on the dimension of the controllable subspace are derived. In addition, a method is proposed that facilitates synthesis of structural and strong structural controllable networks as well as examining preservation of network controllability under structural perturbations.
cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing

Mobile sensing is a promising sensing paradigm in the era of Internet of Things (IoT) that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper — The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch. The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.
Longitudinal changes in macroinvertebrate assemblages in the Meuse River: anthropogenic effects versus natural change

A collaborative study among three nations (France, Belgium, Netherlands) along the Meuse River developed a consistent approach for collecting and interpreting macroinvertebrate data. Specific mesohabitats were sampled in 16 locations along an 800‐km stretch of this lowland regulated river. The objective was to assess the ‘river health’ using macroinvertebrate communities as indicators of biological and ecological variation in space. The main changes in assemblages were investigated using multimetric and multivariate approaches. The authors examined relationships between faunal variations and both physico‐chemical gradients and man‐made disturbances. We related species traits to faunal changes and habitat characteristics.
Determination of temperature coefficient and transient electrical characteristics of a cooled and non-cooled solar module

Experimental investigation of temperature dependence of solar cell characteristics presented by this electronic document. The voltage and current level of electricity, provided by solar cells are highly influenced by the temperature change. The output voltage level of both an idle and a loaded solar cell is decreased by the temperature increase which also leads to power decrease and efficiency deterioration. The intensity increases of the irradiating light linearly increase the current, provided by the power cell which highly increase the temperature of the solar cell and that leads to voltage decrease and efficiency deterioration too. To prevent these processes, it is needed to reduce the temperature of the solar cells. The measured characteristic curves at different temperatures are presented in this paper.