Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Hybrid Modulation Method for the Single-Phase H6 Inverter With Reactive Power Compensation

Transformerless photovoltaic (PV) inverters are more widely adopted due to high efficiency, low cost, and light weight, etc. Many novel topologies and their corresponding modulation methods have been proposed, verified, and put into use, solely focusing on active power injection without leakage current issues. However, some new grid codes require PV inverters to have the ability of injecting reactive power into the utility for grid support. In order to map the challenge, an improved hybrid modulation method is proposed and evaluated for one nonisolated H6 topology as an example. With only simple modification for switching patterns and phase shift for current reference, the variable reactive power generation ability with zero crossing distortion is achieved, while the common mode voltage is also eliminated. The operation modes with the improved hybrid modulation approach are presented in detail and some design considerations are also provided. Extensive results from a 4-kVA prototype along with the SMPLIS simulation verify the proposed hybrid modulation method.
Neuro-Symbolic Execution: The Feasibility of an Inductive Approach to Symbolic Execution

Symbolic execution is a powerful technique for program analysis. However, it has many limitations in practical applicability: the path explosion problem encumbers scalability, the need for language-specific implementation, the inability to handle complex dependencies, and the limited expressiveness of theories supported by underlying satisfiability checkers. Often, relationships between variables of interest are not expressible directly as purely symbolic constraints. To this end, we present a new approach -- neuro-symbolic execution -- which learns an approximation of the relationship as a neural net. It features a constraint solver that can solve mixed constraints, involving both symbolic expressions and neural network representation. To do so, we envision such constraint solving as procedure combining SMT solving and gradient-based optimization. We demonstrate the utility of neuro-symbolic execution in constructing exploits for buffer overflows. We report success on 13/14 programs which have difficult constraints, known to require specialized extensions to symbolic execution. In addition, our technique solves $100$\% of the given neuro-symbolic constraints in $73$ programs from standard verification and invariant synthesis benchmarks.
Polynomial Chaos-Based Approach to Yield-Driven EM Optimization

Yield-driven optimization is important in microwave design due to the uncertainties introduced in the manufacturing process. For the first time, we extend in this paper the use of polynomial chaos (PC) approach from electromagnetic (EM)-based yield estimation to EM-based yield optimization of microwave structures. We first formulate a novel objective function for yield-driven EM optimization. By incorporating the PC coefficients into the formulation, the objective function is analytically related to yield optimization variables, which are the nominal point. We then derive the sensitivity formulas of the PC coefficients with respect to the nominal point, following which we derive the sensitivities of the optimization objective function with respect to yield optimization variables. These sensitivities are then used in gradient-based optimization algorithms to find the optimal yield solution iteratively. The proposed objective function requires fewer EM simulations to provide reliable yield representation than that in the conventional Monte Carlo-based yield optimization approach. As a result, the number of EM simulations required to find the update direction and suitable step size for the change of the nominal point is reduced at each iteration of optimization. This allows the proposed approach to achieve similar yield increase using much fewer EM simulations or greater yield increase using similar number of EM simulations compared to the conventional yield optimization approach. The advantages of our proposed approach are demonstrated by yield-driven EM optimization of three waveguide filter examples.
Personalized dynamic phantom of the right and left ventricles based on patient-specific anatomy for echocardiography studies — Preliminary results

Dynamic phantoms of the heart are becoming a reality, with their use spread across both medical and research fields. Their purpose is to mimic the cardiac anatomy, as well as its motion. This work aims to create a dynamic, ultrasound-compatible, realistic and flexible phantom of the left and right ventricles, with application in the diagnosis, planning, treatment and training in the cardiovascular field for studies using echocardiography. Here, we focus on its design and production with polyvinyl alcohol cryogel (PVA-C), to be assembled with a pump and an electromechanical (E/M) system in a water tank. Based on a patient-specific anatomical model and produced using a 3D printing technique and molding, the PVA-C phantom mimics the ventricles' natural anatomy and material properties, while the pump and E/M systems mimic the natural movements and pressures. The PVA-C phantom was assessed by imaging and measuring it using a four-dimensional ultrasound machine. The PVA-C phantom demonstrated to be a versatile option to produce patient-specific biventricular models, preserving their shape after manufacturing and presenting good echogenic properties. Both chambers were clearly seen in the ultrasound images, together with the interventricular septum and the myocardial wall. Automated left ventricle measures revealed a decrease of its volume with regard to the designed model (98 ml to 74 ml). Overall, the preliminary results are satisfactory and encourage its use for the abovementioned purposes.
A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback

Enabling users to teach their robots new tasks at home is a major challenge for research in personal robotics. This work presents a user study in which participants were asked to teach the robot Pepper a game of skill. The robot was equipped with a state-of-the-art skill learning method, based on dynamic movement primitives (DMPs). The only feedback participants could give was a discrete rating after each of Pepper's movement executions (“very good,” “good,” “average,” “not so good,” “not good at all”). We compare the learning performance of the robot when applying user-provided feedback with a version of the learning where an objectively determined cost via hand-coded cost function and external tracking system is applied. Our findings suggest that (a) an intuitive graphical user interface for providing discrete feedback can be used for robot learning of complex movement skills when using DMP-based optimization, making the tedious definition of a cost function obsolete; and (b) un-experienced users with no knowledge about the learning algorithm naturally tend to apply a working rating strategy, leading to similar learning performance as when using the objectively determined cost. We discuss insights about difficulties when learning from user provided feedback, and make suggestions how learning continuous movement skills from non-expert humans could be improved.
A Bidirectional Series Z-Source Circuit Breaker

A Z-source series circuit breaker topology, which allows bidirectional power flow and has the ability to autonomously disconnect dc faults, is introduced. This topology allows current flow in the forward and reverse directions through the use of a diode bridge. The diode bridge allows response to faults on either the source or load side with only a single controlled switch. No additional passive components are required when compared with the unidirectional series Z-source circuit breaker topology. Analysis is performed to find the fault conditions that cause the bidirectional circuit breaker to trip when operating in a single-load power system. Then, using the simulation platform MATLAB/Simulink, operation of the bidirectional circuit breaker is evaluated for both source- and load-side faults. To experimentally validate the findings, an experimental prototype has been implemented. This prototype is used to confirm the circuit breaker's function in both the forward and reverse directions in addition to its response to source-side faults.
Diagnosis of Meningitis Caused by Pathogenic Microorganisms Using Magnetic Resonance Imaging: A Systematic Review

Introduction: Bacterial meningitis is an acute infectious inflammation of the protective membranes covering the brain. Its early diagnosis is vital because of its high morbidity and mortality. It is mostly diagnosed by a gold standard diagnostic tool i.e. Cerebrospinal Fluid (CSF) analysis. However, it is sometimes difficult and or impossible to do this procedure and an alternative diagnostic tool is needed. Contrast enhanced magnetic resonance imaging can detect the pus or other changes in subarachnoid space. But our optimal aim is to use an imaging method without using contrast to be useable and available in more specific condition. Methods: This study aimed to survey the role of non-contrast Magnetic Resonance Imaging (MRI) in the diagnosis of the bacterial meningitis. MEDLINE/PubMed Central, Web of Science and Scopus were searched without time period and language limitation until March 2017. We found 6410 papers in our initial search. After assessing the content of the papers based on Cochrane library guidelines and inclusion/exclusion criteria, 6 relevant studies were included in the systematic review. All of included studies were observational studies. Results: MRI studies demonstrated that Fluid Attenuation Inversion Recovery (FLAIR) and Diffusion-Weighted Image (DWI) MR imaging among all MRI modalities can detect some abnormalities compatible with bacterial meningitis. FLAIR and DWI-MR imaging are potentially useful to diagnose bacterial meningitis and can be used in emergent condition in which bacterial meningitis is highly suspicious and the other diagnostic tools are not available or feasible.
Efficient Block Matching for Removing Impulse Noise

A number of block-based image-denoising methods have been presented in the literature. Those methods, however, are generally adapted to denoising the Gaussian noise, and subsequently do not show good performance for denoising random-valued impulse, and salt-and-pepper noise. We propose an efficient block-based image-denoising method, which is devised specially for fast denoising of impulse noise. The method first constructs a set of array pointers to image blocks containing a specific pixel value at a specific location. With this scheme, finding of blocks similar to a given block can be done by considering only the blocks pointed by the pointers corresponding to the pixel values of the block without comparing all the blocks in the input image. The experimental results show that the proposed method can achieve superior denoising performance in terms of computational time and signal-to-noise ratio measure.
Autophagic Degradation Deficit Involved in Sevoflurane-Induced Amyloid Pathology and Spatial Learning Impairment in APP/PS1 Transgenic Mice

The adverse effects of anesthetics on elderly people, especially those with brain diseases are very concerning. Whether inhaled anesthetics have adverse effects on Alzheimer’s disease (AD), which is the most common form of dementia with brain degenerative changes, remains controversial. Autophagy, a crucial biological degradation process, is extremely important for the pathogenesis of AD. In this study, the inhaled anesthetic sevoflurane elicited many enlarged autolysosomes and impaired the overall autophagic degradation in the hippocampus of an AD mouse model, which is involved in the accumulation of amyloid-β (Aβ) and spatial learning deficits. However, rapamycin treatment counteracted all these effects. The results suggested that inhaled anesthetics may accelerate the pathological process of AD, and enlarged autolysosomes may be a new marker for prediction and diagnosis of the neurotoxicity of anesthetics in AD.
A Modular-Designed Three-Phase High-Efficiency High-Power-Density EV Battery Charger Using Dual/Triple-Phase-Shift Control

In this paper, an enhancement-mode GaN high-electron mobility transistor (HEMT)-based 7.2-kW single-phase charger was built. Connecting three such single-phase modules to the three-phase grid, respectively, generates a three-phase ∼22-kW charger with the <inline-formula><tex-math notation="LaTeX">$>{\text{97}}\% $</tex-math></inline-formula> efficiency and <inline-formula><tex-math notation="LaTeX">$>{\text{3.3}}-{\rm{kW}/ \rm{L}}$</tex-math> </inline-formula> power density, superior to present Si-device-based chargers. In addition to GaN HEMTs with fast-switching transitions yielding high efficiency, the proposed charger employs the dc/dc stage to control the power factor and power delivery simultaneously, yielding little dc-bus capacitance and thereby high power density. To secure the soft switching for all switches within full voltage and power ranges, a variable switching frequency control with dual phase shifts was adopted at high power, and a triple phase shift was employed to improve the power factor at low power. Both control strategies accommodated the wide input range (80–260 VAC) and output range (200–450 VDC). A closed-loop control for the three-phase charger was realized to minimize the output current ripple and balance the power among three single-phase modules. Experimental results validated this design.
Flow adaptation in serious games for health

This work studies the relevance of flow in gameplay adaptability and how it may offer a better gaming experience, since it provides a better enjoyment of an activity. We developed a first-person shooter video game that adapts its in-game difficulty and environmental settings based on a representation of the mental state of the user to keep a balance between the skills of the player and the challenge of the game. The mental state of the player is measured with their physiological signals, namely the heart rate and the beta band of the brainwaves, and we distinguish the mental state of the player with an accuracy of 87%. We also conducted an evaluation using self-perceived flow and in-game scores as metrics to compare the mental state-based adaptability with a performance-based version. Results show that the latter provided a better gaming experience, suggesting that further research is needed to fully understand the relation of flow and gameplay on FPS games.
A system approach to aircraft optimization

Mutual couplings among the mathematical models of physical phenomenon and parts of a system such as an aircraft complicate the design process because each contemplated design change may have far reaching consequences throughout the system. This paper outlines techniques for computing these influences as system design derivatives useful in both judgmental and formal optimization purposes. The techniques facilitate decomposition of the design process into smaller, more manageable tasks and they form a methodology that can easily fit into existing engineering optimizations and incorporate their design tools.
The relationship between metal electrode's surface roughness and charge injection characteristics at high temperatures

Generally, the central conductor or conducting electrodes will be polished during the manufacture process according to certain standards. Yet even if the electrodes are appropriately treated, the contact between the central conductor and insulation material will not be a perfect contact. There will be air gaps at certain places. How will the air gaps between metal electrodes and insulation materials influence the charge injection behaviors at the interface, is an interesting topic. To study this issue systematically, firstly, the PEA system is improved and modified in order to measure the space charge characteristics in a larger temperature range. Secondly, aluminum electrodes with the same size are polished by sandpapers with different mesh numbers to obtain different surface roughness. Thirdly, a confocal microscope is used to observe the three-dimensional morphology of electrodes with different surface roughness. Then the space charge injection behaviors are measured and analyzed at 80 C with different electrodes. The research provide results that show the relationship between surface roughness of electrodes and the charge injection characteristics, which is of great significance to the manufacture and production of metal electrodes in the electrical engineering industry.
Novel BI data architectures

Most modern companies utilize business intelligence (BI) platforms and tools to acquire new insights into their customers and operations and consequently make better business decisions. The traditional approach to BI consists of a data warehouse containing historical data records, an OLAP engine for faster and more complex data analysis, and various reporting and visualization tools used to present the results. With the advent of big data and cloud technologies, the BI approaches have started to diverge to accommodate various new usage scenarios. Examples for such scenarios include small-scale analytics, which can be performed offline on personal computers, and large-scale, often near real-time, big data analytics. This paper gives an overview of the novel BI approaches and associated BI solutions, proposes architectural dimensions used to classify these systems, and presents a feature comparison with respect to the proposed dimensions.
Electrical properties of a graphene nanoplatelets composite as interposer for electronic packages

This paper deals with the electrical characterization of a graphene-based material that can be proposed for realizing novel interposers to improve the electrical performance of the electronic packages. The material is a low-cost version of the graphene, realized with commercial graphene nanoplatelets, and thus potentially suitable for a large mass production. An electrical characterization of its complex permittivity in the microwave range is here provided, by using a technique based on the measurement of the scattering parameters on a microstrip-like test vehicle. A Drude model is used to retrieve the values of the equivalent complex permittivity. The results demonstrate that this material may outperform the silicon so far used for interposers.
On Frege’s ‘Modes of Presentation’ in Signs

Abstract Gottlob Frege introduced a new dimension to the characterization of signs - that of ‘mode of presentation’. The same referent may be represented by different signs and in different manners, revealing it with dissimilar depth and profundity. The mode of presentation varies due to the fact that the same denoted object changes in the course of time and under alternating circumstances, but also because people designating it have unequal means for doing so. Herein I undertake to explicate these facts as they occur in semiotic reality.
Quasi-Square-Wave Modulation of Modular Multilevel High-Frequency DC Converter for Medium-Voltage DC Distribution Application

In a direct current (dc) distribution network, the modular multilevel high-frequency dc converter (MDCC) can achieve electrical isolation, voltage conversion and power transmission between the low- and medium-voltage dc buses. In this paper, quasi-square-wave (QSW) modulation, which avoids the $dv/ dt$ stress problem and maintains a high dc voltage utilization, is applied in MDCC, and the mathematical model of QSW modulation with a dual-phase-shift (DPS) scheme is established. Based on the model, the influences of staircase rising and falling process in QSW modulation on MDCC performance, including the power characteristic, current stress, switching characteristic, and efficiency characteristic, are analyzed. The influences of QSW modulation under DPS scheme on MDCC performance are also investigated. Moreover, to achieve a comprehensive optimization of MDCC performance, a multiobjective optimized control strategy based on QSW modulation under DPS scheme is proposed. Finally, a MDCC prototype is set up and the experimental results verify the correctness and effectiveness of the analysis and proposed strategy.
Predictive analysis based approach for optimal warehouse product positioning

Building a successful warehouse management system encompasses solving many problems of different nature to reshape the general workflow and ensure improvements in terms of resource management. In order for such a system to be accepted and used by a logistics company, those solutions need to be presented through a simple, adaptable and most importantly, a feasible software solution. One of the aspects that needs to be covered while building a warehouse management system is the optimal product placement in the warehouse. If the products are strategically placed, all the other improvement strategies like stock to picking zone item transfer and item picking order become more efficient and easier to implement. In this paper, tactics and issues regarding the optimal product placement in a warehouse are analyzed in detail through a real-world case study. Solutions of this problem largely differ for new, empty warehouses and for already operative warehouses not able to carry out a stocktaking process. Approaches for both possible situations are proposed and tested out on two different warehouses in a medium-to-large logistics company.
Herbaspirillum seropedicae Differentially Expressed Genes in Response to Iron Availability

Herbaspirillum seropedicae Z67 is a nitrogen-fixing endophyte that colonizes many important crops. Like in almost all organisms, vital cellular processes of this endophyte are iron dependent. In order to efficiently acquire iron to fulfill its requirements, this bacterium produces the siderophores serobactins. However, the presence in its genome of many others iron acquisition genes suggests that serobactins are not the only strategy used by H. seropedicae to overcome metal deficiency. The aim of this work was to identify genes and proteins differentially expressed by cells growing in low iron conditions in order to describe H. seropedicae response to iron limitation stress. For this purpose, and by using a transcriptomic approach, we searched and identified a set of genes up-regulated when iron was scarce. One of them, Hsero_2337, codes for a TonB-dependent transporter/transducer present in the serobactins biosynthesis genomic locus, with an unknown function. Another TonB-dependent receptor, the one encoded by Hsero_1277, and an inner membrane ferrous iron permease, coded by Hsero_2720, were also detected. By using a proteomic approach focused in membrane proteins, we identified the specific receptor for iron-serobactin internalization SbtR and two non-characterized TonB-dependent receptors (coded by genes Hsero_1277 and Hsero_3255). We constructed mutants on some of the identified genes and characterized them by in vitro growth, biofilm formation, and interaction with rice plants. Characterization of mutants in gene Hsero_2337 showed that the TonB-dependent receptor coded by this gene has a regulatory role in the biosynthesis of serobactins, probably by interacting with the alternative sigma factor PfrI, coded by gene Hsero_2338. Plant colonization of the mutant strains was not affected, since the mutant strain normally colonize the root and aerial part of rice plants. These results suggest that the strategies used by H. seropedicae to acquire iron inside plants are far more diverse than the ones characterized in this work. In vivo expression studies or colonization competition experiments between the different mutant strains could help us in future works to determine the relative importance of the different iron acquisition systems in the interaction of H. seropedicae with rice plants.