Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic representation of Slovak words

: The ﬁrst problem one encounters when trying to apply analytical methods to text data is probably that of how to represent it in a way that is amenable to operations such as similarity, composition, etc. Recent methods for learning vector space representations of words have succeeded in capturing semantic using vector arithmetic, however, all of these methods need a lot of text data for representation learning. In this paper, we focus on Slovak words representation that captures semantic information, but as the data source, we use a dictionary, since the public corpus of the required size is not available. The main idea is to represent information from the dictionary as a word network and learn a mapping of nodes to a low-dimensional space of features that maximize the likelihood of preserving network neigh-bourhoods of word nodes.
Approach to Automatic Segmentation of Atherosclerotic Plaque in B-images Using Active Contour Algorithm Adapted by Convolutional Neural Network to Echogenicity Index Computation

: The presented paper is dedicated to image processing of ultrasound B-images in neurosonology. Atherosclerotic plaques in in-vitro B-images are analyzed in this study. The content is divided into two core parts. The ﬁrst one is focused on computing Echo-Index value inside the atherosclerotic plaque as a deﬁned Region of Interest. From achieved results is obvious that the Echo-Index is well-reproducible value in general. Totally of 278 images were analyzed by two non-experienced observers and by an experienced sonographer to validate the result. Basic statistical descriptors were calculated to judge the level of agreement. In this part, the ROI were selected manually. The second part is focused on approach to automatic selection of the ROI. Manual drawing of the border is time-consuming. Our idea is to use active contour al-gorithm (ACM; Active Contour Model) to eliminate the black background from the displayed plaque. Using ACM can be useful way to select ROI automatically. The main issue is to separate the shape of the plaque from neighbor structures, especially from the bottom of the tube. To adapt ACM, the principle of
Non-canonical role of E3 ligase RIPLET in innate immunity as a co-receptor for RIG-I

Ubiquitin (Ub) and its E3 ligases play diverse biological roles, from proteasomal degradation to innate immune signaling. The conventional view posits that E3 ligases function primarily through conjugating Ub to their substrate molecules. We report here that RIPLET, an essential E3 ligase in antiviral immunity, promotes the antiviral signaling activity of the viral RNA receptor RIG-I through both Ub-dependent and –independent manners. RIPLET utilizes its dimeric structure and the bivalent binding mode to preferentially recognize RIG-I preoligomerized on dsRNA. RIPLET can also adopt an alternative binding mode for filamentous oligomers of RIG-I assembled on longer dsRNAs, cross-bridging RIG-I filaments in both ubiquitin-dependent and–independent manners. The resultant receptor clustering leads to the formation of aggregate-like cytosolic granules and the stimulation of RIG-I-mediated antiviral signaling in a RNA-length dependent manner. These observations show the unexpected role of an E3 ligase as a co-receptor that directly participates in receptor oligomerization and ligand discrimination. This study also highlights previously unrecognized mechanisms by which an E3 ligase induces receptor clustering and signal amplification, and offers insights into the unique cellular function of membrane-less granule assembly. One-sentence summary RIPLET functions as a molecular glue to assemble signaling scaffolds for the antiviral innate immune receptor RIG-I.
Scotopic microperimetric sensitivity and inner choroid flow deficits as predictors of progression to nascent geographic atrophy

Background/Aims To assess the role of microperimetric retinal sensitivity (MPRS) and inner choroid flow deficits (IC FD) in predicting the development of incomplete retinal pigment epithelial and outer retinal atrophy (iRORA) in intermediate AMD (i-AMD). Methods Thirty eyes with i-AMD evaluated at the Doheny-UCLA Eye Centres were enrolled in this prospective IRB-approved study. Subjects underwent several diagnostic tests: (a) 6×6 mm swept-source optical coherence tomography angiography (SS-OCTA) with the IC slab used to quantify the FDs, (b) 20°×20° spectral-domain optical coherence tomography (SD-OCT) to monitor progression to iRORA and (c) scotopic MPRS within an area of 18° centred on the fovea. All subjects were followed-up for 24 months. The baseline IC FD and MPRS were correlated with the development of iRORA. At 24-month follow-up, the stage of AMD was re-assessed and the eyes were divided into two sub-groups based on the development of iRORA. Results Twenty-eight eyes completed the 2-year follow-up. At baseline, the mean MPRS was 13.40±4.66 dB and the mean IC FD was 27.55±8.67%. The morpho-functional regression showed a significant correlation between baseline MPRS and IC FD and the development of iRORA within 24 months (R2=0.744, p<0.05). A Kaplan–Meier survival curve was fit to determine the cumulative incidence of iRORA over the 24 months. Conclusions A lower MPRS and greater IC FD at baseline were predictors of progression to iRORA in eyes with i-AMD. These parameters may be useful biomarkers for risk stratification and prognostication.
Simultaneous Speciation of Arsenic, Selenium, and Chromium by HPLC-ICP-MS

Title: Simultaneous Speciation of Arsenic, Selenium, and Chromium by HPLC-ICP-MS. By: Ruth E. Wolf, Suzette A. Morman, Jean M. Morrison, and Paul J. Lamothe, U.S. Geological Survey, Minerals Resources Program, Box 25046, MS 964D, Denver Federal Center, Denver CO 80225; rwolf@usgs.gov An adaptation of an analytical method developed for chromium speciation (Wolf and others, 2008) has been utilized for the simultaneous determination of As(III), As(V), Se(IV), Se(VI), Cr(III), and Cr(VI) species using high performance liquid chromatography (HPLC) separation with ICP-MS detection. Reduction of interferences for the determination of As, Se, and Cr by ICP-MS is a major consideration for this method. Toward this end, a Dynamic Reaction Cell (DRC) ICP-MS system was used to detect the species eluted from the chromatographic column. A variety of reaction cell gases and conditions may be utilized, and the advantages and limitations of the gases tested to date will be presented and discussed. The separation and detection of the As, Se, and Cr species of interest can be achieved using the same chromatographic conditions in less than 2 minutes by complexing the Cr(III) with EDTA prior to injection on the HPLC column. Practical aspects of simultaneous speciation analysis will be presented and discussed, including issues with HPLC sample vial contamination, standard and sample contamination, species stability, and considerations regarding sample collection and preservation methods. The results of testing to determine the method’s robustness to common concomitant element and anion effects will also be discussed. Finally, results will be presented using the method for the analysis of a variety of environmental and geological samples including waters, soil leachates and simulated bio-fluid leachates.
Perioperative Renoprotection: Clinical Implications

Acute kidney injury (AKI) remains a common complication in the perioperative setting affecting patients’ short- and long-term outcome. Because therapeutic options are restricted to the use of renal replacement therapy, preventive strategies have become increasingly important. Several substances have been investigated for preventing AKI with limited to no effects. The lacking effectiveness of all these therapies might be caused by the fact that the therapy was started too late. In all the studies, therapy was initiated once a reduced kidney function occurred. In contrast to the classical functional biomarkers, new renal biomarkers allow to identify kidney damage without a loss of function thus enabling the implementation of preventive measures at the stage of renal stress. The most promising preventive strategy to date seems to implement a bundle of supportive measures in patients at high risk for AKI as recommended by the Kidney Disease: Improving Global Outcomes (KDIGO) group. This strategy includes the avoidance of nephrotoxic drugs and contrast agents, avoidance of hyperglycemia, optimization of perfusion pressure and hemodynamics with consideration of a functional hemodynamic monitoring, and close monitoring of renal function in patients at high risk for AKI. This review discusses new renal biomarkers for identifying kidney damage, the background of why the different measures of the KDIGO bundle might positively affect renal function and prevent the development of AKI, and presents the current literature of biomarker-based approaches in AKI.
Systemic Inflammatory Response Syndrome After Surgery: Mechanisms and Protection

The immune system is an evolutionary hallmark of higher organisms that defends the host against invading pathogens and exogenous infections. This defense includes the recruitment of immune cells to the site of infection and the initiation of an inflammatory response to contain and eliminate pathogens. However, an inflammatory response may also be triggered by noninfectious stimuli such as major surgery, and, in case of an overshooting, still not comprehensively understood reaction, lead to tissue destruction and organ dysfunction. Unfortunately, in some cases, the immune system may not effectively distinguish between stimuli elicited by major surgery, which ideally should only require a modest inflammatory response, and those elicited by trauma or pathogenic infection. Surgical procedures thus represent a potential trigger for systemic inflammation that causes the secretion of proinflammatory cytokines, endothelial dysfunction, glycocalyx damage, activation of neutrophils, and ultimately tissue and multisystem organ destruction. In this review, we discuss and summarize currently available mechanistic knowledge on surgery-associated systemic inflammation, demarcation toward other inflammatory complications, and possible therapeutic options. These options depend on uncovering the underlying mechanisms and could include pharmacologic agents, remote ischemic preconditioning protocols, cytokine blockade or clearance, and optimization of surgical procedures, anesthetic regimens, and perioperative inflammatory diagnostic assessment. Currently, a large gap between basic science and clinically confirmed data exists due to a limited evidence base of translational studies. We thus summarize important steps toward the understanding of the precise time- and space-regulated processes in systemic perioperative inflammation.
Crosstalk Suppression in Structured Light Free-Space Optical Communication

Using space as an additional degree of freedom is one possible solution to cope with future bandwidth issues. In free-space optics (FSO), spatial multiplexing of structured light modes is limited by the impact of the atmospheric turbulence. Therefore, beating the effects of turbulence is a major problem for structured light-based FSO communication. Here, we model the propagation of Laguerre-Gaussian beams using a modified von Kármán turbulence model with time-dependent turbulence phase screens. We equally investigate the performance of a zero-forcing pre-coding technique to mitigate the effects of turbulence in a full-duplex Laguerre-Gaussian mode based FSO. In the modeling, we account for various limiting factors, including phase estimation errors, noise, imperfect, and outdated channel-state information on the pre-coding approach.
Filter Design and Optimization of Electromechanical Actuation Systems Using Search and Surrogate Algorithms for More-Electric Aircraft Applications

In this article, the dc filter design and optimization problem is studied for dc electrical power distribution systems onboard more-electric aircraft. Component sizing models are built to serve as the basis of the optimization whose objectives are mass and power loss of this filter. A categorization strategy of search and surrogate algorithms is proposed and used for the target multiobjective optimization problem (MOOP). A genetic algorithm is utilized as a search algorithm to identify potential best solutions based on a set of filter sizing functions (subject to constraints). In addition, two machine learning (ML) algorithms are considered as surrogate algorithms to address the same optimization problem. In the ML training process, a constraint violation model is applied since there are various constraints in optimization, and this kind of classification model is relatively difficult to train. A support vector machine is applied for the constraint violation model; after that, two artificial neural networks are trained as the final surrogate model for mapping design variables to filter performance. To address these issues, a novel category of search and surrogate algorithms is proposed. Both algorithms are explored to solve the filter MOOP, and their optimization results are compared at the end.
Demagnetization Performance Enhancement of Heavy Rare Earth Free Permanent Magnet Machines

This paper proposes a 90% HRE-free hybrid rotor concept to enhance the demagnetization performance of a 100 kW motor for traction applications. The demagnetization analysis is presented for three cases- for a motor with 100% HRE-free, 90% HRE-free, and finally with a 100%-HRE magnet. The proposed concept divides each magnet into several segments, and use HRE magnets at the most demagnetization prone top corners of a rectangular magnet. For the proposed hybrid rotor, magnets can be safe from irreversible demagnetization up to 150°C even at 100% negative d–axis current and three-phase short-circuit fault. The hybrid rotor uses approximately 90% HRE-free PM content. Moreover, the hybrid rotor reduces the magnet loss by 90%, which helps alleviate the challenging thermal management problem in a rotary environment.
A Novel Spoke-type Asymmetric Rotor Interior PM Machine

This paper proposes a novel spoke-type asymmetric interior permanent magnet (Spoke-AIPM) machine featured by spoke-type symmetrical permanent magnets (PM) and asymmetric flux barrier structure. Without introducing any extra cost comparing with symmetrical designs using the same PMs, this asymmetric rotor topology shows torque enhancement due to magnetic-field-shifting (MFS) effect in the asymmetric rotor structure, which reduces the difference between the current angles for maximum PM and reluctance torque components. Three machines, i.e. the proposed Spoke-AIPM, a spoke-type IPM (Spoke-IPM) with symmetrical flux barrier, and a conventional Spoke-IPM, are designed with the same stator, rotor diameters and PMs. Effects of rotor flux barrier dimensions on maximum torque, constant power speed range (CPSR) performance, and open-circuit flux density for topologies are investigated and the optimal designs are obtained. The electromagnetic performances of three machines are compared, which confirms the torque enhancement of the proposed Spoke-AIPM topology and reveals the enhancement is achieved by MFS effect.
Digital Platform to promote sustainable mobility and COVID-19 infections reduction: a use case in the Guadalajara metropolitan area

This work proposes a digital platform based in a multi-agent system to plot the different mobility alternatives to counteract the agglomerations in public transport and therefore decrease COVID-19 infections in the GDL. Following the recommendations of the World Health Organization (WHO) in the face of the health emergency of COVID-19, mainly keeping a healthy distance, the GDL can integrate sustainable mobility as the public bicycle system and reduce the users in regular transport routes to safe levels. For that endeavor, we develop an analysis of the behavior of the trips in the public transport of Guadalajara to explore the possibility of substituting the short transfers of bus travel by bicycle travels using the existing public bicycle infrastructure. We introduce a multi-agent simulation to plot different scenarios of mobility moving bicycles and buses. In this preliminary work, we show possibilities of the simulation integrating as variables not only the risk of COVID-19 infection but also the impact on economy and traffic reduction, CO2 Footprint, and health achieved with this multimodal mobility simulation. Also, the simulation can help to incentivize safer and efficient mobility strategies in the public transport system to reduce the use of private vehicles.
An End-to-End Approach to Reconstructing 3D Model From Image Set

Large-scale 3D reconstruction from imagery has received much attention from the computer vision community. However, recovering 3D structures from 2D images is a notoriously complex process that requires expertise with often limited results. This paper presents an end-to-end 3D reconstruction system that can produce high-quality 3D models from a set of unordered image collections. Our workflow is a typical 3D reconstruction architecture that consists of structure from motion (SFM), multi-view stereo (MVS), and surface reconstruction, and can automatically recover desirable 3D models without any interactive operations. Finally, a comprehensive experiment is conducted on several benchmark datasets to assess the presented system. Experimental results show that the presented system achieves significant improvements in reconstruction accuracy and completeness over the existing state-of-the-art approach.
Improving Fingerprint Indoor Localization Using Convolutional Neural Networks

Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the “fingerprint image” required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters’ location. Additionally, the observer’s coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters’ locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.
Energy Management Supervisory Controller Embedded-Board for Islanded Hybrid AC/DC Microgrids

Microgrid systems have received much attention as they can make a way for more efficient power flows in the modern power systems. This paper presents a hybrid AC/DC microgrid to reduce the processes of multiple conversions in an individual AC or DC microgrid. In this paper, a new method of the supervisory controller is proposed to autonomously control and monitor for an islanded microgrid and to maintaining more stable energy flows with maximum utilization of renewable resources. The supervisory controller enables the microgrid system to operate in different modes of operation by the coordination control algorithms in the different components for smooth transfer of power between AC and DC networks. With the energy management option of the supervisory controller, this supervisor also provides an efficient load scheduling method to meet varying power supply needs and manage the sources and allow the microgrid to make a good operation and decisions. Therefore, in this paper, an islanded AC/DC microgrid has been modeled and simulated to identify the relevant technical issues involved in the operation of a microgrid system based on renewable power generation units using MATLAB/Simulink, and a new method of an energy management system is proposed for monitoring the islanded microgrid.
On Minimizing Analog Variation Errors to Resolve the Scalability Issue of ReRAM-Based Crossbar Accelerators

Crossbar accelerators with a resistive random-access memory (ReRAM) are a promising solution for accelerating neural network applications. The advantages of achieving high computation throughput per watt make ReRAM-based crossbar accelerators become a potential solution for accelerating inference operations in the Internet of Things and edge devices. Due to the analog variation errors, the launched ReRAM-based crossbar accelerators can only perform well when each ReRAM cell is used to represent a limited number of data bits. To make such ReRAM-based crossbar accelerators applicable in wide application scenarios, several proposed researches target at binary neural networks and focus on the chip designs in relieving the implementation challenges on computation accuracy for realizing single-bit ReRAM-based crossbar accelerators. Even though several small-sized ReRAM-based crossbar accelerators are announced, the scalability issue hinders ReRAM-based crossbar accelerators from being scaled up. That is, when there are more and more wordline in an ReRAM-based crossbar accelerator, the analog variation error is amplified and thus seriously degrades the computation accuracy. In this work, we propose an adaptive data manipulation strategy to substantially reduce analog variation errors so as to fill up the gap on scaling up the ReRAM-based crossbar accelerators. In particular, a weight-rounding design is proposed to manipulate data to minimize overlapping variation so that the number of wordlines can be scaled up. In addition, an input subcycling design is proposed to further trade tolerable errors with neural networks’ execution time. Moreover, a bitline redundant design is proposed to trade acceptable space overhead for eliminating the analog variation errors. The emulation experiments show that the proposed adaptive data manipulation strategy can improve the accuracy in running MNIST and CIFAR-10 by $1.3\times $ and $2.6\times $ with nearly no management penalty and hardware cost. The experimental results also show the close-to-ideal-case accuracy by substantially reducing analog variation errors.
A Morphological Analysis Method-Based Model of Assessing Territories for Underground Parking Lots

The paper is devoted to the development and testing of a model that formalizes and supports decision-making process regarding the appropriateness of using territory (geological environment) for urban underground construction. A morphological model of territorial development for underground city planning that combines the influence of structural and functional factors with impact of geological environment, was designed and tested on real construction sites in Kyiv; the modified morphological analysis method was applied for risk estimation of urban development of underground space; systemic characteristics of urban territories were obtained, which show the favorability of a site for underground construction of a parking lot.