Full Power Research Articles

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

WEBGPU: A NEW GRAPHIC API FOR 3D WEBGIS APPLICATIONS

Abstract. As smart city applications increase today, the importance of web-based representations of data that serve as inputs for these applications, such as 3D city models and digital twins, is growing. Since 2011, GPU hardware has greatly advanced, and detailed 3DCMs and digital twins have increased the size of the data to be displayed, which in turn increases performance requirements. WebGL, which couldn’t fully harness the capabilities and power of modern GPUs, began to struggle to meet the increasing performance demands over time and become a bit outdated. Consequently, a new graphic API named WebGPU was developed by W3C as a response to limitations of WebGL and went live with Chrome 113. Now, we have a brand-new graphic API called WebGPU which harness the full power of modern GPUs and more performant than WebGL. As WebGPU is a new graphic API, its potential enhancements and what it can bring over WebGL in the terms of WebGIS have not been examined yet. Hence, the main idea and contribution of this work is to investigate WebGPU in real-world use cases for WebGIS applications and discuss what it brings over WebGL. For this purpose, a side-by-side performance comparison has been made. The comparisons have been made in the terms of API differences and performance. And finally, an experiment has been carried out how much data can be rendered can be rendered at minimum 60fps in both APIs. The experiments show that WebGPU is more low-level and way more performant than WebGL and it has a lot to offer in the terms of WebGIS applications.

Optimal Design of a Single-Phase Bidirectional Rectifier

This article outlines the comprehensive design and control approach for a single-phase bidirectional rectifier (SPBR) used in bidirectional charging of electric vehicle batteries. The operational parameters of the inverter are determined through a thorough analysis of all switching sequences to accurately assess power losses, considering the type of switching device chosen in each case, enabling proper component sizing, and understanding converter efficiency. An exclusive electronic control circuit is examined, governing two converter operation modes: boost rectifier with power factor correction (PFC) and sine pulse inverter width modulation (SPWM) with a minimum number of adjustments made automatically. One problem that arises when addressing the design of an SPBR is determining the operating frequency. To address this issue, this study offers to conduct a comparative analysis of losses using various power devices and magnetic circuits to determine the optimal operating frequency for achieving maximum energy efficiency. To validate the design’s feasibility, a prototype with 10 kW output power was constructed, achieving a peak efficiency of approximately 97.5% in both directions, unity power factor (PF), and total harmonic distortion (THD) of less than 7% during full power operation.

How to solve a binary cubic equation in integers

Abstract Given any polynomial in two variables of degree at most three with rational integer coefficients, we obtain a new search bound to decide effectively if it has a zero with rational integer coefficients. On the way we encounter a natural problem of estimating singular points. We solve it using elementary invariant theory but an optimal solution would seem to be far from easy even using the full power of the standard Height Machine.

A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains.

The Position of Children's Characters in Children's Animation Postcolonialism Studies

Children's literature has now begun to shift to digital media, known as children’s animation. Instead of children's literature being created for children, these works are written, edited and distributed by adults. This shows that adults (parents) have full power over the work that children will consume. Colonialism theory then questions what it means to write for children. This research appears with the real assumption of where the child is positioned in children’s cyber literature. The postcolonial approach is used to verify this assumption. The results show that children are treated as objects, a means to endure real problems faced by adults. It appears that writing for children in addition to exploring is also stuffing and imposing the will for the needs of adults: guiding children, training children through the process of reaching civilization which is again the power of adults to define it.

Predicting stock market movements using neural networks: A review and application study

In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field.

First-principle based predictions of the effects of negative triangularity on DTT scenarios

Abstract Plasmas with negative triangularity (NT) shape have been recently shown to be able to achieve H-mode levels of confinement in L-mode, avoiding detrimental edge localised modes. Therefore, this plasma geometry is now studied as a possible viable option for a future fusion reactor. Within this framework, an NT option is under investigation for the full power scenario of the Divertor Tokamak Test (DTT) facility, under construction in Italy, with δ t o p = − 0.32 / δ b o t t o m ≃ 0.02 top/bottom triangularity values at the separatrix. The transport properties of this scenario are studied in this work. Gyrokinetic GENE simulations and integrated modelling using ASTRA with the quasi-linear trapped gyro-Landau fluid (TGLF) model have been performed. The emerging picture from the ASTRA-TGLF runs with boundary conditions at ρ t o r = 0.94 is that, in the L-mode NT option, the larger peaking of the kinetic profiles in the edge region is not sufficient to recover the loss of the PT H-mode pedestal, and reach similar central temperature values. Two additional shapes are also considered, obtained by flipping the triangularity of the scenarios, to single out the effect of the triangularity sign. A negligible ‘direct’ effect of the triangularity is found for the L-mode, while a small beneficial effect is observed for the H-mode. The ASTRA-TGLF results are validated by GENE and TGLF stand-alone at two selected radii. GENE shows ITG dominant micro-instability and explains the small beneficial effect of the NT for the H-mode as due to a strong reduction of the heat fluxes, when reversing the triangularity, with a relatively high T i stiffness. An improvement of the predicted performances of the NT DTT scenario could come from ρ tor ≳ 0.9 , as indicated by some recent experiments at the tokamak à configuration variable (TCV) and ASDEX Upgrade.

Highly Thermally Conductive Aramid Nanofiber Composite Films with Synchronous Visible/Infrared Camouflages and Information Encryption

AbstractThe development of highly thermally conductive composites that combine visible light/infrared camouflage and information encryption has been endowed with great significance in facilitating the application of 5G communication technology in military fields. This work uses aramid nanofibers (ANF) as the matrix, hetero‐structured silver nanowires@boron nitride nanosheets (AgNWs@BNNS) prepared by in situ growth as fillers, which are combined to fabricate sandwich structured thermally conductive and electrically insulating (BNNS/ANF)‐(AgNWs@BNNS)‐(BNNS/ANF) (denoted as BAB) composite films by “filtration self‐assembly, air spraying, and hot‐pressing” method. When the mass ratio of AgNWs@BNNS to BNNS is 1 : 1 and the total mass fraction is 50 wt %, BAB composite film has the maximum in‐plane thermal conductivity coefficient (λ∥ of 10.36 W/(m ⋅ K)), excellent electrical insulation (breakdown strength and volume resistivity of 41.5 kV/mm and 1.21×1015 Ω ⋅ cm, respectively) and mechanical properties (tensile strength of 170.9 MPa). 50 wt % BAB composite film could efficiently reduce the equilibrium temperature of the central processing unit (CPU) working at full power, resulting in 7.0 °C lower than that of the CPU solely integrated with ANF directly. In addition, BAB composite film boasts adaptive visible light/infrared dual camouflage properties on cement roads and jungle environments, as well as the function of fast encryption of QR code information within 24 seconds.

Highly Thermally Conductive Aramid Nanofiber Composite Films with Synchronous Visible/Infrared Camouflages and Information Encryption.

The development of highly thermally conductive composites that combine visible light/infrared camouflage and information encryption has been endowed with great significance in facilitating the application of 5G communication technology in military fields. This work uses aramid nanofibers (ANF) as the matrix, hetero-structured silver nanowires@boron nitride nanosheets (AgNWs@BNNS) prepared by in situ growth as fillers, which are combined to fabricate sandwich structured thermally conductive and electrically insulating (BNNS/ANF)-(AgNWs@BNNS)-(BNNS/ANF) (denoted as BAB) composite films by "filtration self-assembly, air spraying, and hot-pressing" method. When the mass ratio of AgNWs@BNNS to BNNS is 1 : 1 and the total mass fraction is 50 wt %, BAB composite film has the maximum in-plane thermal conductivity coefficient (λ∥ of 10.36 W/(m ⋅ K)), excellent electrical insulation (breakdown strength and volume resistivity of 41.5 kV/mm and 1.21×1015  Ω ⋅ cm, respectively) and mechanical properties (tensile strength of 170.9 MPa). 50 wt % BAB composite film could efficiently reduce the equilibrium temperature of the central processing unit (CPU) working at full power, resulting in 7.0 °C lower than that of the CPU solely integrated with ANF directly. In addition, BAB composite film boasts adaptive visible light/infrared dual camouflage properties on cement roads and jungle environments, as well as the function of fast encryption of QR code information within 24 seconds.

Structuring Cu Membrane Electrode for Maximizing Ethylene Yield from CO2 Electroreduction.

Electrocatalytic ethylene (C2 H4 ) evolution from CO2 reduction is an intriguing route to mitigate both the energy and environmental crises; however, to acquire industrially relevant high productivity and selectivity at low energy cost remains to be challenging. Membrane assembly electrode has shown great prospect and tailoring its architecture for maximizing C2 H4 yield at minimum voltage with long-term stability becomes critical. Here a freestanding Cu membrane cathode is designed and constructed by electrochemically depositing mesoporous Cu film on Cu foam to simultaneously manage CO2 , electron, water, and product transport, which shows an extraordinary C2 H4 Faradaic efficiency of 85.6% with a full cell power conversion efficiency of 33% at a current density of 368mAcm-2 , heading the techno-economic viability for electrocatalytic C2 H4 production.

INCREASING THE OUTPUT VOLTAGE OF PWM INVERTER USING HARMONIC DISTORTION

By adding a measure of third harmonic to the output of each phase of three-phase inverter, it is possible to obtain a line-to-line output voltage that is 15 percent greater than that obtainable when pure sinusoidal modulation is employed.The line-to-line voltage is undistorted. The method permits the inverter to deliver an output voltage approximately equal to the voltage of the ac supply to the inverter. Thus an induction motor of standard rating with respect to the ac supply to the inverter can deliver very nearly full power at rated speed when supplied from the inverter. This is achieved without pulse dropping or any other form of mode changing.

Energy scaling of a narrowband, periodically poled LiNbO3, nanosecond, nonresonant optical parametric oscillator

We demonstrate that 3-mm-thick, periodically poled LiNbO3 enables energy scaling of a nonresonant optical parametric oscillator operated in the narrowband mode with a volume Bragg grating at the signal wavelength. Utilizing the full available pump power at 1064 nm, we obtained maximum average powers of 2.25 and 2.08 W for the signal (1.922 µm) and idler (2.383 µm) pulses at 10 kHz, at a total conversion efficiency of 32.8%, which represents a fourfold increase in terms of peak powers over our previous work. The signal and idler spectral linewidths were ∼1nm, with pulse lengths of ∼6ns and an idler beam propagation factor of ∼5.

Toward Useful Quantum Kernels

AbstractSupervised machine learning is a popular approach to the solution of many real‐life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault‐tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near‐term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault‐tolerant quantum computer.It turns out that, for the latter class, the benefit offered by quantum computation can be shown rigorously using quantum kernels, whereas the approach based on near‐term quantum computers is very unlikely to bring any advantage if implemented in the form of hybrid algorithms that delegate the hard part (optimization) to the far more powerful classical computers.

(359) Vasectomy Trends in Western NY Following Dobbs v. Jackson Women’s Health Organization

Abstract Introduction On June 24, 2022, the United States Supreme Court ruled on Dobbs v. Jackson Women’s Health Organization (Dobbs) that the Constitution of the United States does not confer a right to abortion. The court’s decision overruled both Roe v. Wade (1973) and Planned Parenthood v. Casey (1992), giving individual states the full power to regulate any aspect of abortion not protected by federal law. Objective Our study aims to identify the difference, if any, in the number of vasectomies performed in Western NY before and after the ruling on Dobbs v. Jackson Women’s Health Organization. Methods We performed a retrospective chart review of 2428 patients who had vasectomies in Western New York from August 2021 – March 2023. Leveraging a dataset containing de-identified patient information, we compared the rates of all men undergoing vasectomies in the six-month period before and after the ruling. Vasectomy case information collected by urologists in Buffalo dating back to 1996 was collected and tabulated using Excel. Statistical analyses were performed to compare differences in both the overall change in frequency of vasectomies among all men in the six-month period before and after the court decision, as well as by specific demographic groups. Statistical analyses were performed in R and SAS. Results Men with no children had a statistically significant increase (p=0.009) in the number of vasectomies in the six-month period after the Dobbs decision (n=138) compared to before (n=98). Rates of vasectomies pre- and post- Dobbs were organized by age quartiles (<33.20, 33.20-36.65, 36.66-40.59, ≥40.60). In the “No Child” group, men with ages <33.2 (p=0.0137) and ages 33.2-36.65 (p=0.0394) had significant increases in frequency of vasectomy after the court decision. Conclusions Our data demonstrates that in the wake of Dobbs v. Jackson Women’s Health Organization (2022), the number of vasectomies among men with no children significantly increased after the court decision. There were significant increases in the number of vasectomies within different age groups of men without children following the decision. Future aims of this study will seek to create statistical models to determine the underlying reasons that influenced the observed change in frequency of vasectomies following the court decision in men without children. Disclosure No.

The system of government in colonial Kenya

The object of the study of this work is the government system of colonial Kenya (1890-1950's). The subject of the study is public authorities, their powers, area of responsibility and features of functioning in East African realities. The author has carried out a detailed analysis of the administrative structure, the law enforcement system, and key management links at the central and regional levels. Special attention was paid to the issues of urban development based on the principle of racial segregation and local self-government, as well as the problem of combating specific crime. The article focuses on a number of non-trivial aspects of the British approach to organizing the work of government agencies, including special recruitment, active lawmaking, largely based on borrowing decisions from British India, and also the introduction of the institute of chiefs at the local level. The methodological basis of the article was historical-legal and problem-chronological approaches, in addition, the author turned to general scientific methods of analysis and deduction. The British system of government was built up in Kenya during the 1900s and 1920s, acquiring features of completeness in the main institutions by the 1930s. Before the arrival of the European colonialists, there was no civilizational basis for the existence of state entities. Therefore the leading role at all levels of the administrative structure belonged to people of European origin. The full power in the colony actually belonged to the governor, who unconditionally pursued the policy of the metropolis. This state of affairs was consolidated by a cultivated ideological paradigm that presupposes the superiority of the British and Europeans in general in all spheres of society, including management. The strength and coherence of the work of the state mechanism was ensured by the homogeneity of the nomenclature. By the time of gaining independence in 1963, the newly-minted Kenyan authorities inherited a completely efficient system of government bodies from the colonial regime.

The contribution of massive haloes to the matter power spectrum in the presence of AGN feedback

ABSTRACT The clustering of matter, as measured by the matter power spectrum, informs us about cosmology, dark matter, and baryonic effects on the distribution of matter in the universe. Using cosmological hydrodynamical simulations from the cosmo-OWLS and BAHAMAS simulation projects, we investigate the contribution of power in haloes with various masses, to the full power spectrum, as well as the power ratio between baryonic and dark matter only (DMO) simulations for a matched (between simulations) and an unmatched set of haloes. We find that the presence of AGN feedback suppresses the power on all scales for haloes of all masses examined (1011.25 ≤ M500, crit ≤ $10^{14.75}\, \mathrm{M_\odot }/h$), by ejecting matter from within $r_{500,\mathrm{c}}\,$ to $r_{200,\mathrm{m}}\,$ and potentially beyond in massive haloes (M500, crit ≳ $10^{{13}}\, \mathrm{M_\odot }/h$), and likely impeding the growth of lower-mass haloes as a consequence. A lower AGN feedback temperature changes the behaviour of high-mass haloes (M500, crit ≥ $10^{{13.25}}\, \mathrm{M_\odot }/h$), damping the effects of AGN feedback at small scales, $k\, {{\gtrsim }}\, {{4}}\, h\mathrm{\, Mpc^{-1}}$. For $k\, {{\lesssim }}\, {{3}}\, h\mathrm{\, Mpc^{-1}}$, group-sized haloes ($10^{{14\pm 0.25}}\, \mathrm{M_\odot }/h$) dominate the power spectrum, while on smaller scales the combined contributions of lower-mass haloes to the full power spectrum rise above that of the group-sized haloes. Finally, we present a model for the power suppression due to feedback, which combines observed mean halo baryon fractions with halo mass fractions and halo-matter cross-spectra extracted from DMO simulations to predict the power suppression to per cent level accuracy down to $k\, {{\approx }}\, {{10}}\, h\mathrm{\, Mpc^{-1}}$ without any free parameters.

Beyond Gaia DR3: Tracing the [α/M] – [M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks

Context. In June 2022, Gaia DR3 provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200 million spectra. Thus, stellar spectra are projected to be produced on an ‘industrial scale’, with numbers well above those for current and anticipated ground-based surveys. However, one-third of the published spectra have 15 ≤ S /N ≤ 25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised. Aims. We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining Gaia-RVS spectra with the full set of Gaia products and high-resolution, high-quality ground-based spectroscopic reference datasets. Methods. We developed a hybrid convolutional neural network (CNN) that combines the Gaia DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g) as well as overall [M/H]) and chemical abundances ([Fe/H] and [α/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. Results. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886 080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N =15. We managed to characterise the [α/M] - [M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. Conclusions. This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of Gaia-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry.

Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

A partial soft-switching SiC-based ANPC single-phase inverter with loss model-based dead time optimization for grid-tied PV systems

Single-phase string inverter has been widely applied to grid-tied photovoltaic (PV) rooftop applications for its renewable energy. However, the inherent attribute of intermittency in solar energy may induce instability and unqualified power. To meet the grid-interconnection standards, high demands of efficiency and harmonic distortion need to be imposed on DC/AC inverters. Conventional soft-switching inverters used to sacrificing auxiliary resonant circuits to exchange for soft switching. This requires additional costs as well as sufficient dead time to discharge junction capacitances of all switches, which exacerbates total harmonics distortion (THD) induced by dead-time effect. To improve, this paper proposes a partial soft-switching silicon carbide (SiC) -based active neutral-point-clamped (ANPC) inverter, which possesses high efficiency, eligible dead time, and no auxiliaries. 650-V SiC MOSFET is adopted for its smaller output capacitance compared with Si device. Hybrid PWM modulation is utilized to achieve partial soft switching. Whereas, the soft switching realization range and power losses highly depend on the dead time. To achieve less power loss and improved dead time effect, this paper proposes a loss model-based dead time algorithm through a Power Loss VS. Dead Time mathematical loss model. Moreover, the mechanism of the dead time affected by soft switching is explored. The transition modes, dead time configurable boundaries, and design procedure are also analyzed in detail. A 1-kW prototype is demonstrated with the input of 800 V and the rated output of 220 V/ 4.5 A. Its efficiency could reach 99.6% with THD=0.95% at full power level.