Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Grown Vertically Oriented Graphene Coating on Copper by Plasma-Enhanced CVD to Form Superhydrophobic Surface and Effectively Protect Corrosion

Graphene exhibits great potential for the corrosion protection of metals, because of its low permeability and high chemical stability. To enhance the anticorrosion ability of Cu, we use plasma-enhanced chemical vapor deposition (PECVD) to prepare a vertically oriented few-layer graphene (VFG) coating on the surface of Cu. The Cu coated with VFG shows superhydrophobic surface with a contact angle of ~150°. The VFG coating is used to significantly increase the anticorrosion ability, enhanced by the chemical stability and the unique geometric structure of vertically oriented graphene. The corrosion rate of VFG-Cu was about two orders of magnitude lower than that of bare Cu. This work highlights the special synthesized way of PECVD and superhydrophobic surface of vertical structures of graphene as coatings for various applications.
The effect of backward noise on lexical tone discrimination in Mandarin-speaking amusics

Congenital amusia is a neurogenetic disorder, affecting music pitch processing. It also transfers to the language domain and negatively influences the perception of linguistic components relying on pitch, such as lexical tones. It has been well established that unfavorable listening conditions impact lexical tone perception in amusics. For instance, both Mandarin- and Cantonese-speaking amusics were impaired in tone processing under simultaneously noisy conditions. Backward noise is one of the adverse listening conditions, but its interference mechanism is distinct from the simultaneous noise. Therefore, it warrants more studies to explore whether and how backward noise affects tone processing in amusics. In the current study, eighteen Mandarin-speaking amusics and 18 controls were tested on discrimination of Mandarin tones under two conditions: a quiet condition involving relatively low-level processing and a backward masking condition involving high-level processing (e.g., tone categorization) where a native multi-talker babble noise was added to target tones. The results revealed that amusics performed similarly to controls in quiet conditions, whereas their performance was found poorer in backward masking conditions. These findings shed light on how adverse listening environments influence amusics’ lexical tone processing and provided further empirical evidence that amusics may be impaired in the high-level phonological processing of lexical tone.
Charging characterization of a high‐capacity lithium‐sulfur pouch cell for state estimation: An experimental approach

LithiumSulfur (LiS) battery is a next‐generation technology, which is promising for applications that require higher energy density in comparison to the available lithium‐ion batteries. Along with the ongoing research on LiS cell material development and manufacturing to improve this technology, engineers are also working on LiS battery management systems (BMS). The existing BMS algorithms, which are developed for lithium‐ion batteries, are not useable for the LiS mainly due to its constant voltage plateau during the discharge phase. As a result, the LiS system has poor observability during discharge, which limits the BMS functionality that can be implemented from discharge information alone, and it is worth considering if information from charging is useful. In this study, the charging behavior of a high‐capacity pouch cell is investigated and characterized for the purpose of state estimation in a BMS. Several tests are conducted on prototype LiS cells at different temperatures and age levels. An online feature extraction method is then used in combination with a classification technique to estimate the cell's states during charging. The proposed charging estimators can provide accurate initialization for state estimation accuracy during discharge by providing good estimates of the post‐charging state of charge (ie, around 3%) and capacity after fading (ie, around 2%).
The internal syntax of DP

An indicator to be secured inside of the glass cover of a meter to show when the meter is rotated as a unit, the indicator includes a frame fixed to the meter and a flag member in a normal position hidden behind a portion of the frame and which flag is movable between the normal position to a second position wherein the flag is visible whenever the meter is rotated about an axis perpendicular to the face on which the indicator is mounted and the indicator includes magnetic means to hold the flag in the second position so that one inspecting the meter will know that it has been physically rotated or tampered with.
Finer-grained Modeling units-based Meta-Learning for Low-resource Tibetan Speech Recognition

Tibetan is a typical under-resourced language due to its relatively smaller population. Although a character-based end-to-end (E2E) automatic speech recognition (ASR) model with transfer learning and multilingual training strategies has mitigated the problem of low resources, it often meets overfitting problem. Recently meta-learning performs great in solving overfitting problem. However, the widely-used coarse-grained modeling units are not significantly correlated to their pronunciation, which limits the performance improvement of the low-resource ASR system. Furthermore, meta-learning consists of a meta-training period and fast self-adaption on the target language, and the past meta-training period is lack target language-specific information. Therefore, we propose a novel E2E low-resource Lhasa dialect ASR model based on the finer-grained modeling units and transfer learning with reference to the properties of Chinese Pinyin. Chinese Pinyin and Tibetan decomposed radicals are more related to pronunciation than characters are, which can compensate for more acoustic information in low-resource situations. Furthermore, Tibetan modeling units are utilized in both meta-training and fast self-adaption processes to offer language-specific information to solve the low-resource problem. Experiments show that our proposed method achieves a 54.9% relative character error reduction rate than the baseline system.
Electron beam velocity effects with kappa distribution function on kinetic Alfven waves in dusty plasma

: The effect of electron beam on dispersion relation, and growth rate, perpendicular wave and marginal instability of the kinetic Alfven waves with kappa distribution function in a low β homogeneous plasma is discussed by investigating the trajectories of the charge particles. Particle aspect approach is adopted to investigate the trajectories of charged particles in the electromagnetic field of kinetic Alfven wave in the presence of kappa distribution function. Kinetic Alfven wave with kappa distribution function in the presence of beam effects are investigated. Expressions are found for dispersion relation, growth rate and growth length for dust particle of charge. The results are interpreted for the space plasma parameter appropriate to the auroral acceleration region of earth's magnetosphere. Kinetic effects of electrons and ions are included to study kinetic Alfven wave because both are important in the transition region.
LG-Tree: An Efficient Labeled Index for Shortest Distance Search on Massive Road Networks

With the development of mobile Internet technology, the road network in the real world is becoming larger and more complex, and the real-time response to the shortest distance query has become a high requirement for many industrial applications. Many existing approaches, such as G-tree or G*-tree, have been proposed to answer such search problems, however, these approaches are not efficient enough in dealing with very large graphs. To this end, we propose a novel index called LG-tree, which partitions the large graph into sub-graphs, and then indexes these subgraphs using a balanced tree. For each leaf node of LG-tree, a Distance Inverted File (DIF) is constructed, and these DIFs preserve all the connectivity information between the border vertices on the original graph. Inspired by the state-of-the-art label methods, we propose a novel hierarchy computing approach for each border vertex of LG-tree. Based on DIF and the hierarchy of border vertices, the border vertex list for each border is established, which is convenient for us to calculate the shortest distance and path between a query vertex <inline-formula> <tex-math notation="LaTeX">$v_{q}$ </tex-math></inline-formula> and a target vertex <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>. Specifically, the number of levels of a vertex hierarchy increases dramatically as the graph size increases, and calculating vertex labels on a very large graph is an NP-hard problem. In order to further improve the calculation efficiency of the shortest distance between vertices, a heuristic method is proposed to limit the level of vertex hierarchy. In addition, a stage-based dynamic programming search method is proposed to divide all search situations between <inline-formula> <tex-math notation="LaTeX">$v_{q}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> into three cases to guarantee the efficiency of the shortest distance search. Extensive experiments are conducted to show that LG-tree and the stage-based method have better performance than the state-of-the-art approaches.
The disk vanishes

Recently published observations of VY Scl stars in their low, long-intermediate and high states confirm our model (Hameury & Lasota 2002) according to which accretion disks in such systems must vanish when their temperature corresponds to thermally unstable configurations. An observational confirmation of the hypothesis that the disk disappearance is caused by magnetic effects would have important consequences for cataclysmic variable evolution models.
Sepsis among Neonates in a Ghanaian Tertiary Military Hospital: Culture Results and Turnaround Times

In this study, we described the bacterial profile, antibiotic resistance pattern, and laboratory result turnaround time (TAT) in neonates with suspected sepsis from a tertiary-level, military hospital in Accra, Ghana (2017–2020). This was a cross-sectional study using secondary data from electronic medical records. Of 471 neonates clinically diagnosed with suspected sepsis in whom blood samples were collected, the median TAT from culture request to report was three days for neonates who were culture-positive and five days for neonates who were culture-negative. There were 241 (51%) neonates discharged before the receipt of culture reports, and of them, 37 (15%) were culture-positive. Of 471 neonates, twenty-nine percent (n = 139) were bacteriologically confirmed, of whom 61% (n = 85) had late-onset sepsis. Gram-positive bacterial infection (89%, n = 124) was the most common cause of culture-positive neonatal sepsis. The most frequent Gram-positive pathogen was coagulase-negative Staphylococcus (55%, n = 68) followed by Staphylococcus aureus (36%, n = 45), of which one in two were multidrug resistant. The reasons for large numbers being discharged before the receipt of culture reports need to be further explored. There is a need for improved infection prevention and control, along with ongoing local antimicrobial resistance surveillance and antibiotic stewardship to guide future empirical treatment.
Nutritional Education to the Nutritional Maternal Knowledge and Iron Intake among Toddlers with Anemia

BACKGROUND: In general, maternal knowledge about nutrition and health affects food habits, influencing the child’s nutritional or iron intake. One effort to improve children’s iron status is by providing maternal nutrition education. AIM: The purpose of this study was to analyze the effect of a nutrition education intervention on maternal knowledge and toddlers’ iron intake to prevent anemia among children under five. Paired t-test was used to analyze differences in knowledge, nutrient intake, consumption of iron sources, and hemoglobin (Hb) status of children before and after nutrition education interventions. METHODS: This study was a Quasi Experiment with a one-group pre-posttest design. The subjects of this study were 40 toddlers consisting of two groups, namely, 20 toddlers in the intervention group and 20 toddlers in the control group. Mother’s knowledge about anemia, while nutrient intake, food sources containing iron were analyzed using Paired T-test. RESULTS: The results of this study indicated a significant effect of a nutrition education intervention on improvement of maternal knowledge (p = 0.000), energy intake (p = 0.001), carbohydrates (p = 0.038), fat (0.047), iron (0.031), and consumption of iron food sources including chicken (p = 0.000) and fish (p = 0.005). However, no significant effect was identified on the Hb level of toddlers (p = 0.66). CONCLUSION: Nutrition education intervention significantly affected the mother’s knowledge and toddlers’ iron intake but did not affect the Hb levels of toddlers.
Detection of Lotmaria passim and Crithidia mellificae in Selected Bumblebee Species

Bumblebees (Bombus spp.) are an essential element of the ecosystem and the global economy. They are valued pollinators in many countries around the word. Unfortunately, there has been a decline in the bumblebee population, which is attributed to, among others, pathogens and reduced access to food due to the loss of natural nesting sites. Lotmaria passim and Crithidia mellificae, protozoan pathogens of the family Trypanosomatidae, commonly infect bumblebees, including in Poland. In this study, a Polish population of bumblebees was screened for L. passim and C. mellificae. The experiment was performed on 13 adult bumblebees belonging to 4 species: B. lapidarius, B. lucorum, B. pascuorum, and B. terrestris. Protozoa of the family Trypanosomatidae were identified by PCR. Only L. passim was identified in one B. pascuorum individual. Further research is needed to confirm the effect of concurrent pathogens on the decline of bumblebee populations.
Natural Resource Economics Under the Rule of Hotelling

A method for stabilizing a spinal motion segment by boring and excising an opening in a disc nucleus, inserting a fabric bag having a pore size between about 0.25 and 5.0 mm, packing the bag with a graft medium until the bag is a rigid self-retaining shape, then closing the bag. The nucleus is excised by inserting an expandable cutting tool in order to cut an area greater than the access opening. A further spinal stabilization includes positioning a flexible bag containing graft medium against the exterior of the lamina bone of the adjacent vertebra. The exterior lamina bone is prepared by roughening to present a bleeding surface to hasten bone growth.
Advancing coastal systems resilience research: Improving quantification tools through community feedback

and the projections for the future of America’s coasts a cacophony of risks and vulnerabilities — from increasing coastal populations, to climate change, cyber, human-induced threats, and budgetary restrictions. These issues are the present-day challenges for coastal professionals; the design, selection, and management of various solutions must incorporate resilience to recover rapidly after disturbances and to overcome these challenges in the long term. This paper outlines a joint effort by researchers from the U.S. Army Corps of Engineers’ (US-ACE) Engineer Research and Development Center (ERDC) and the National Oceanic and Atmospheric Administration (NOAA) to test and evaluate a suite of tools to quantify resilience for a case-study site in Mobile Bay, Alabama, and surround-ing Mobile and Baldwin counties. The process of validating tools that have only been applied conceptually as acceptable for practice in the field is often evolutionary, with periods of adaptation and incremental improvements, especially when the tools are evaluating a concept as broad as resilience. This learning process is referred to ABSTRACT A large number of initiatives exist at the federal level to provide resilience tools to communities and decision makers, but quantitative assessments of resilience are limited. There is a need for objective evaluation of coastal systems that includes the community, economy, and environment. To meet this need, U.S. Army Corps of Engineers researchers at the Engineer Research and Development Center developed a tiered methodology to assess and quantify resilience for rapid assessment (Tier 1), planning and operations-level studies (Tier 2), and engineering applications (Tier 3). This paper describes the application of Tiers 1 and 2 with collaboration from the National Oceanic and Atmospheric Administration, Sea Grant, the University of Rhode Island, and the U.S. Committee on the Marine Transportation System during a pilot workshop in Mobile Bay, Alabama in March 2015. The results of this study centered on feedback received from two groups of professionals: workshop participants from the south Alabama region and a subsequent review of workshop findings by experts convened by the National Academy of Sciences. Feedback on the approach and community needs are required to best advance tools to quantify coastal resilience on variable spatial and temporal scales.
Wiener solution considering cross-spectral term between echo and near-end speech for acoustic echo reduction

: This paper introduces a frequency-domain acoustic echo reduction process based on a new Wiener-ﬁltering method taking into account the cross-spectral term between the acoustic echo and the near-end speech. The conventional echo reduction method based on Wiener ﬁltering estimates the gain based on the assumption that the cross-spectral term of the echo and the near-end speech is zero because the acoustic echo and the near-end speech are statistically uncorrelated. However, this assumption does not always hold true in practice because the gain is estimated in a very short period where the amount of statistical data, which is used to calculate the ensemble averages of the observed signals, is insuﬃcient. As a result, the conventional method occasionally causes the perceptual degradation of sound quality during a double-talk situation; therefore, the performance is still not suﬃcient. Our goal was to accurately calculate the echo-reduction gain to decrease the speech distortions produced by the echo-reduction process. The proposed method solves a least mean square error of the Wiener-ﬁltering method by taking into account the cross-spectral term between the echo and the near-end speech to obtain a better echo-reduction gain. The performance of this method was demonstrated by objective and subjective results in which speech distortions were decreased
Computational Understanding of Narratives: A Survey

Storytelling, and the delivery of societal narratives, enable human beings to communicate, connect, and understand one another and the world around them. Narratives can be defined as spoken, visual, or written accounts of interconnected events and actors, generally evolving through some notion of time. Today, information is typically conveyed over online communication mediums, such as social media and blogging websites. Consequently, the act of narrative delivery itself has shifted from simply imparting information through self-contained structures such as books, to more fragmented structures, such as social media websites, where evolving story events are constructed over multiple online sources. Ubiquitous online conversation can manifest into sophisticated narratives that have the potential to influence wide-spread user interpretations of cultural sentiments, attitudes, values, as well as geopolitical events and facts. As a result, narratives are actively being used as strategic tools for shaping local events, promoting collective opinions, and asserting ideologies and propaganda, making them sources of interest for identifying themes, intentions, and goals across multiple communities and potential adversaries. Identifying fragmented narratives, extracting thematic and temporal components that constitute evolving narratives, and locating signs of active rhetoric framing tactics, are difficult to detect and analyze without large-scale automation. This problem can be addressed through the use of natural language understanding technologies. Our goal is to document and discuss methods to efficiently construct, extract, and detect evolving online narratives. The novel contribution of this paper is the formal collation and documentation of such technologies and research areas, as well as extensive discussion on open research challenges and goals in the definition, identification, construction, generation, and representation of online narratives. To our knowledge, there is currently no existing formal documentation that organizes and provides extended discussion on narrative understanding research areas and open challenges.
Economic dispatch of an islanded microgrid

Microgrids are increasingly becoming popular to improve energy access and increase the resiliency of weakly connected rural networks. The economic operation of these microgrids with renewable energy resources is key for maximizing the benefits. The objective of this paper is to implement the economic dispatch of a microgrid using quadratic programming, considering the active and reactive power capability of the renewable energy resources. The open distribution system simulator (OpenDSS) is used for obtaining the load flow of the distribution network, and the converged solution is given as input to MATLAB for optimization. The simulation is carried out for one day with a variation of hourly load, solar PV radiation, and wind in both grid-connected and islanded modes under six different cases. The methodology is implemented on a modified IEEE-13 node test feeder distribution system. The simulation result shows that the microgrid with distributed generation (DG) capable of supplying both active and reactive power under islanded conditions is economical when compared to the grid-connected mode. The novelty of the work is that it considers the capability of distributed generation to supply both active and reactive power. That can make it fully autonomous as it can meet the hourly load requirement of the network under islanding mode as per IEEE Standard 1547.4.
Energy Technologies Area Lawrence Berkeley National Laboratory Can machine learning predict fuel properties accurately?

: High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Experimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regression models with molecular descriptors and will compare results of different estimators. This study describes the ﬁrst steps towards a performance prediction tool for bio-based alternative fuels.