Miracle-dz/UniFAR-FTUs huggingface dataset:
Unified Selective Harmonic Elimination Control for Four-Level Hybrid-Clamped Inverters

A unified selective harmonic elimination (SHE) control for four-level hybrid-clamped (4L-HC) inverters is presented in this article. With this unified strategy, all four-level switching patterns and the corresponding switching angles can be obtained simultaneously by solving one group of unified four-level SHE equations. Therefore, the optimal switching pattern of each modulation index with the designed optimization goal can be evaluated, and the best overall output performance is achieved. In order to ensure the proper operation of the 4L-HC inverter, the voltages across the three-phase flying capacitors and three dc-link capacitors must be controlled and balanced at one-third of the dc-link voltage. The proposed voltage control method is based on redundant switching states and introducing a slight variation to the precalculated switching angles, which extends or limits the conduction time of capacitors depending on voltage deviation and current direction. The effect of switching angle variations on the harmonic performance is also studied. Simulation and experimental results are presented to confirm the validity of the unified model and the proposed voltage-balancing strategy. Comparisons with phase-shifted pulsewidth modulation (PWM) are also presented to demonstrate that the implemented SHE-PWM could significantly reduce the switching frequency while keeping the capacitor voltages well balanced within the expected band limits.
Environment and climate change during the late Holocene in Hjaltadalur, Skagafjörður, northern Iceland

ABSTRACT We present an overview of the local environmental development of the valley of Hjaltadalur, situated in Skagafjörður, northern Iceland. The aim of this study is to increase the knowledge about the valley region before and during human settlement in the ninth century. Four mires were investigated after which the Viðvík peat bog was selected as the main site for evaluating changes in climate and landscape. The master core from Viðvík (V-325) was dated and studied further through sediment analysis, loss-on-ignition (LOI), and pollen analysis. According to the age-depth model, based on three radiocarbon dates and analysis of two tephra layers, the 325 cm long Viðvík core comprises approximately 5500 years. In the pollen percentage record, there is a decrease in birch (Betula) and an increase in grass (Poaceae) in the central part of the core, between Hekla 3 horizon at c. 2800 BP and the next dated level at c. 2000 BP. This change corresponds well with previously outlined environmental fluctuations, showing a transition from warm and dry climate to cool and humid climate at this time. Human activity is mainly reflected by a distinct peak in Lactucae pollen in the uppermost part of the core. This change in vegetation corresponds with earlier studies, showing that the vegetation changed dramatically after the colonization of Iceland in the ninth century (during Landnám period, 870–930 AD). The present study shows that a decline in birch started well before human settlement, although the subsequent Viking Age and later settlements continued the deforestation trend.
An Improved Deep Neural Network Model Based on Searching Space Limitation for Object Localization

Image object localization is the primary research direction in computer vision. A lots of algorithms had been proposed based on the ResNet structure and good results have been obtained on mainstream datasets. However, these methods still have the problem of too large ROI search space on weakly supervised data. This paper proposes a method that further limits the search space by directly extracting the high-level parameters of ResNet. The better search results and search efficiency are achieved and the candidate ROI search space is better reduced. The space information of the original images can be retained and training/testing time can be saved. The experimental results on the benchmark datasets show that this network structure has good object localization accuracy under weakly supervised data and the search efficiency is improved compared to other methods.
Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.
The Role of Extracellular Vesicles in the Progression of ALS and Their Potential as Biomarkers and Therapeutic Agents with Which to Combat the Disease

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that impairs motor neuron function, leading to severe muscular atrophy. The non-cell autonomous and heterogeneous nature of the disease has so far hindered attempts to define ALS etiology, leaving the disease incurable and without effective treatments. Recent studies have focused on the pathologic role of intercellular communication between nerve cells to further our understanding of ALS pathophysiology. In this chapter, we summarize recent works investigating the role of extracellular vesicles (EVs) as a means of cellular crosstalk for ALS disease propagation, diagnosis, and treatment. There is growing evidence that EVs secreted by the majority of mammalian cells serve as effective biomolecule carriers to modulate recipient cell behavior. This underscores the need to understand the EV-mediated interplay that occurs within irreversibly degenerating nervous tissue in ALS patients. Addition-ally, we highlight current gaps in EV-ALS research, especially in terms of the pathologic role and responsibilities of specific EV cargos in diseased cells, specificity issues associated with the use of EVs in ALS diagnosis, and the efficacy of EV-mediated treatments for the restoration of diseased neuromuscular tissue. Finally, we provide suggestions for future EV-ALS research to better understand, diagnose, and cure this inveterate disease.
Modeling the Aerosol Extinction in Marine and Coastal Areas

Sea salt aerosol (SSA) significantly affects the atmospheric albedo, the radiation balance of the Earth, and has consequences for global and regional climate. There is currently no reliable radiative large-scale aerosol model of the marine and coastal atmosphere. All these lead to uncertainties in radiation boxes, short-term weather forecasts, and climate models and to uncertainties in interpretation of the lidar return data obtained and at the development of new lidar systems. The MaexPro model is shown to calculate adequately the aerosol extinction coefficients <inline-formula> <tex-math notation="LaTeX">$\varepsilon (\lambda)$ </tex-math></inline-formula> and the concentration of SSA particles for different wind regimes, fetches, and humidity. The calculated <inline-formula> <tex-math notation="LaTeX">$\varepsilon (\lambda)$ </tex-math></inline-formula> profiles well agree with the experimental data and with calculations using Advanced Navy Aerosol Model (ANAM) and MODTRAN codes. A new approach in studying the influence of the meteorological parameters on spectral <inline-formula> <tex-math notation="LaTeX">$\varepsilon (\lambda)$ </tex-math></inline-formula> gives a new explanation for previously obtained conflicting experimental data, namely, to the effect of decreasing transparency, manifested in the coastal zone, and makes it possible indirectly to account for the type of air mass (marine or coastal). It is shown that the particle size distribution functions and spectral <inline-formula> <tex-math notation="LaTeX">$\varepsilon (\lambda)$ </tex-math></inline-formula>, calculated using the model with the input parameters corresponding most closely to the conditions of experiment, agree well with observations, using OLAF transmissometer, and calculations using MODTRAN code and the experimental results receiving the same conditions of observations (numerical or field). The results obtained are of interest both in the calculations of radiative characteristics of the atmosphere and in interpretation of the lidar return data and calibration signal when developing new lidar systems.
Noise Propagation Calculations of a Wind Turbine in Complex Terrain

This paper describes numerical noise propagation calculations of a single wind turbine in complex terrain near a town in Central Denmark. The purpose of the work is to estimate the noise level at increasing distances from a single wind turbine and investigate the effect of complex terrain. Results indicate that time varying numerical noise propagation predictions of wind turbines in complex terrain can be achieved by using computational fluid dynamics via large-eddy simulation (LES) and the Technical University of Denmark’s noise propagation tool based on the parabolic equation. The wind turbine is modeled using the actuator line approach. The results from this project will have significant social, environmental, and economic impact, since accurate predictions of wind turbine noise propagation can be used to develop noise mitigation strategies to increase the public acceptance of wind energy.
Ship Detection in SAR Images via Local Contrast of Fisher Vectors

Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms.
Viscous Fingering and Preferential Flow Paths in Heterogeneous Porous Media

Preferential flow channeling and viscous fingering are widely observed phenomena in heterogeneous porous media from the pore scale to the core and reservoir scales. The transition from capillary to viscous fingering with increasing injection rate is also a well‐known phenomenon. Based on the previously observed visual similarity of viscous fingers and preferential single‐phase flow paths in 2‐D simulations, we postulate that these single‐ and two‐phase flow patterns should also be interrelated in 3‐D porous media. Capillary fingering is a manifestation of invasion percolation, a process restricted to the subnetwork of pores obtained from the critical path analysis. We investigated single‐phase flow channeling by applying a method similar to critical path analysis to the flow rate field and compared the subnetwork thus determined to the set of invaded pipes in drainage simulations. Our aim is to quantify the relation between preferential flow paths and viscous fingering, its dependence on pore‐scale heterogeneity and pore connectivity, and ultimately to upscale the network observations to the field scale appropriate for the engineering applications involving drainage in heterogeneous media.
Morphological Diversity for Yield and Its Component Traits in Mungbean [Vigna radiata (L.) Wilczek]

The current experiment was held in Summer season, 2018 at rice upland 1, near rice bore well of Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India. The experiment was done by 36 Mungbean genotypes at rice upland 1, near rice bore well of Bihar Agricultural University, Sabour, Bhagalpur in 2018. The analysis of variance (ANOVA) disclosed highly significant differences for all the traits studied in the experiment among all the genotypes. The genetic diversity was estimated by D2 analysis. The 36 genotypes were grouped into 11 distinct clusters with cluster 4 to cluster 11 consisting of one genotype each. The maximum inter-cluster distance was found between cluster 11 and cluster 3 where maximum intracluster distance was found within cluster 2. The characters like Harvest index, the number of clusters per plant, seed yield per plant and days to maturity contribute maximum towards genetic divergence. The hybridization between the clusters 11 & 3 could give maximum heterosis and better desirable segregants. Original Research Article Krishna et al.; CJAST, 39(4): 34-41, 2020; Article no.CJAST. 50169 35
TRANSMEDIA STORYTELLING EDUTAINMENT EXPERIENCE IN ENGINEERING STUDIES

Transmedia Storytelling Edutainment is a new learning paradigm that develops from the coordinated and strategic use of multiple media/platforms for the communication of a story (content), involving students in the story as prosumers (producers+consumers). Experiential learning combined with story-based curriculums can be a very effective means of educating students. Specifically, Transmedia Storytelling Edutainment is effective in improving engagement, motivation and knowledge retention with students. Experiences have been made with young people in basic education, but they are scarce in university studies and more specifically in technical bachelor’s degrees. Transmedia storytelling is a complex task that requires design considerations of narrative, engagement, and interaction. This paper analyses the structure and characteristics of transmedia experiences in university. Based on the results of the analysis, we propose a transmedia experience in a technical subject of the Degree in Computer Science. This strategy takes into account the following aspects: the story should be convincing and based on realism; the promotion of cooperation and a sense of community be promoted; and that there be multimodality in the learning environments, as well as challenging students to go deeper into the course material.
Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure

Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.
Effects of Upwelling Intensity on Nitrogen and Carbon Fluxes through the Planktonic Food Web off A Coruña (Galicia, NW Spain) Assessed with Stable Isotopes

The input of new nutrients by upwelling in shelf waters, and the associated carbon and nitrogen fluxes, can be traced by their stable isotope signatures in organic matter. Here, we analyze the relationships between upwelling intensity and natural abundance of stable carbon and nitrogen isotopes in seston, sedimented particles, and four plankton size fractions (200 to 5000 µm) sampled monthly during 2010 and 2011 in a seasonal upwelling ecosystem. Upwelling modified the seasonal warming stratification by introducing cold and nutrient-rich waters in subsurface layers, enhancing chlorophyll-a and diatom abundance. Seston and sedimented particles were enriched in heavy nitrogen (but not carbon) isotopes linearly with upwelling intensity, indicating a primary effect of upwelling on phytoplankton production. In contrast, all plankton fractions were enriched in heavy carbon isotopes with upwelling, mainly due to the consumption of diatoms. These results confirm the differential effect of upwelling on nitrogen and carbon fluxes in the plankton food web. Direct effects of the new nitrogen inputs on phytoplankton are less evident with the increase of plankton size as nitrogen is repeatedly recycled, while the enriched carbon of plankton suggests the consumption of diatoms during upwelling. We provide linear equations to assess the influence of changes in upwelling intensity on nitrogen and carbon fluxes in seston and plankton in this ecosystem, as well as to estimate reference baseline values for food web studies.
An Investigation into the Biochemical Effects ofHeavy Metal Exposure on Seaweeds

Abstract Optimised methods, developed in this novel study, were utilised throughout the research. In environmental studies of Newfoundland and Ireland, interspecies and spatial variations in the total protein content and polyphenol levels of seaweeds were demonstrated. A positive correlation between total protein and temperature was established. A downstream increase in polyphenol levels of seaweeds was observed and correlated with a downstream increase in seawater salinity. P. lanosa contained the highest levels of all metals. Levels of Pb2+, Cr3+ and Cu2+ in seaweed were highest for sites in Waterford City. The highest proportions of Pb2+, As3+, Cr3+, Cu2+, Ni2+ and Al3+ were intracellular. Baseline levels of total protein, extracted protein and polyphenols of four different seaweed species varied significantly. P. lanosa contained the highest total protein. F. vesiculosus and A. nodosum yielded the highest levels of polyphenols. Significant interspecies variations in total and intracellular metals were observed. Zn2+, As3+ and Mn2+ were dominant for all species. P. lanosa demonstrated the best overall metal bioaccumulation potential. Regression analysis demonstrated correlations between total protein and Pb2+, Ni2+, Cu2+ and Cd2+. Seasonal variations in levels of Cd2+, total protein, extracted protein and polyphenols of P. lanosa were observed. The highest levels of total protein were yielded in May. February demonstrated the highest extracted protein and November the lowest polyphenols. Increases in protein concentration following Cd2+ exposure were observed for P. lanosa sampled in February. Increases in polyphenol levels following Cd2+ exposure were observed in the November samples. Potentiometric titrations and FTIR analysis demonstrated seasonal variations in the binding potential of P. lanosa. Gel filtration chromatography, HPLC analysis and SDS PAGE demonstrated changes to the molecular weights of protein derived from P. lanosa following heavy metal exposure.
One-Step Synthesis of Graphene, Copper and Zinc Oxide Graphene Hybrids via Arc Discharge: Experiments and Modeling

In this paper, we report on a modified arc process to synthetize graphene, copper and zinc oxide graphene hybrids. The anode was made of pure graphite or graphite mixed with metals or metal oxides. After applying a controlled direct current, plasma is created in the interelectrode region and the anode is consumed by eroding. Continuous and abundant flux of small carbon, zinc or copper species, issued from the anode at a relatively high temperature, flows through the plasma and condenses in the vicinity of a water-cooled cathode leading to few-layered graphene sheets and highly ordered carbon structures. When the graphite rod is filled with copper or zinc oxide nanoparticles, few layers of curved graphene films were anchored with spherical Cu and ZnO nanoparticles leading to a one-step process synthesis of graphene hybrids, which combine the synergetic properties of graphene along with nanostructured metals or semiconducting materials. The as-prepared samples were characterized by Raman spectroscopy, X-ray diffraction (XRD), spatially resolved electron energy loss spectroscopy (EELS), energy filtered elemental mapping and transmission electron microscopy (TEM). In addition to the experimental study, numerical simulations were performed to determine the velocity, temperature and chemical species distributions in the arc plasma under specific graphene synthesis conditions, thereby providing valuable insight into growth mechanisms.
A response to constructive criticism of Social and Emotional Learning

Recent years have seen a rapid expansion of efforts to promote social and emotional learning (SEL) in the nation’s schools. As some observers have pointed out, though, the growth of the SEL movement poses some challenges. In particular, concerns have been raised about the need for clearer definitions of SEL, less hype, more attention to equity, and a more patient approach to gaining public support. Such constructive criticism is welcome, say two longtime leaders of the SEL movement. But it’s important to keep them in perspective and recognize the movement’s strengths, as well.
Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach

Convolutional neural networks (CNNs) demonstrate excellent performance when employed to reconstruct the images obtained by compressed-sensing magnetic resonance imaging (CS-MRI). Our study aimed to enhance image quality by developing a novel iterative reconstruction approach that utilizes image-based CNNs and k-space correction to preserve original k-space data. In the proposed method, CNNs represent a priori information concerning image spaces. First, the CNNs are trained to map zero-filling images onto corresponding full-sampled images. Then, they recover the zero-filled part of the k-space data. Subsequently, k-space corrections, which involve the replacement of unfilled regions by original k-space data, are implemented to preserve the original k-space data. The above-mentioned processes are used iteratively. The performance of the proposed method was validated using a T2-weighted brain-image dataset, and experiments were conducted with several sampling masks. Finally, the proposed method was compared with other noniterative approaches to demonstrate its effectiveness. The aliasing artifacts in the reconstructed images obtained using the proposed approach were reduced compared to those using other state-of-the-art techniques. In addition, the quantitative results obtained in the form of the peak signal-to-noise ratio and structural similarity index demonstrated the effectiveness of the proposed method. The proposed CS-MRI method enhanced MR image quality with high-throughput examinations.