batterydata/paper-abstracts huggingface dataset:
The main focus of this study is to investigate the non-Muslim Malaysians’ attitudes and motivations towards learning Arabic as a foreign language. The study aims at examining the respondents’ attitudes towards learning foreign languages in general, towards learning Arabic language in particular and attitudes towards Arabic native speaker. This study also aims at investigating the motivational orientations among the NMMLAs in learning Arabic as a foreign language. The findings reveal that the NMMLAs’ held a high positive attitudes towards foreign languages, and held a moderate positive attitudes towards Arabic language and its native speakers. As to motivations, the respondents were instrumentally motivated to learn Arabic language with the need to fulfil the university requirement having the highest ranking whereas the intrinsic and integrative motivation came second with the same mean score . By conducting this research, it is hoped to widen the horizon of teaching Arabic in Malaysia, not only to Malay Muslims learners but also to non-Muslims.
This paper builds upon previous research to develop a new mixed integer linear program (MILP) for optimal PV-battery sizing and energy scheduling. Unlike previous formulations, the MILP optimises under both time-of-use (TOU) and demand tariff structures. Optimisation is based on the highest system net present value (NPV). One residential and one commercial customer are used as case studies to contrast optimisation under TOU and demand tariff structures. Optimal PV-battery sizing is not found to be affected by the tariff structures analysed. Optimal solutions under both tariffs prefer larger PV systems coupled with small battery systems. Energy consumption from the grid under TOU tariff optimisation reflects a scaled profile of the consumer’s energy demand curve. Peak consumption from the grid is heavily reduced under demand tariff optimisation to decrease the associated demand charge. In the residential case study, peak grid consumption over one year is reduced from 5.98 kWh to 2.25 kWh under demand tariff optimisation. In the commercial case study, peak grid consumption over one year is reduced from 450.3 kWh to 348.6 kWh. The reduction of peak grid consumption is achieved by using the stored energy in the battery.
The practical applications of Si electrodes in lithium-ion batteries are limited since they undergo large changes in volume during charge and discharge, and consequently become highly deteriorated. A novel binder system holding silicon particles together and preventing disintegration of the electrode during operation hence needs to be developed to enable reliable cycleability. In the current work, such a new polymer binder system, based on poly(acrylic acid) (PAA) and poly(ethylene glycol-co-benzimidazole) (PEGPBI), is developed for silicon anodes. The physical crosslinking using acid-base interactions between PAA and PBI, together with the ion-conducting PEG group, yields physical properties for the resulting PAA-PEGPBI-based anodes that are better than those of electrodes based on the currently available PAA binder, and yields good cell performances. A Si-based electrode with high loading levels of 1.0–1.3 mg cm−2 (0.7–0.91 Si mg cm−2) is reliably manufactured using specifically PAA-PEGPBI-2, which is made with 2 wt% of PEGPBI relative to PAA, and shows a very high capacity value of 1221 mAh g−1 at a rate of 0.5 C after 50 cycles, and a high capacity value of more than 1600 mAh g−1 at a high rate of 2 C.
The purpose of this study was to identify the different selective collection systems implemented in Spanish cities and to analyse the efficiency and extent to which the targets proposed by current law are met in each case. After defining the indicators to be used to quantify the efficiency of a selective collection system, a survey was designed to gather the information needed to calculate them. This survey was sent out to all Spanish cities with a population of over 50,000 inhabitants. Four different selective collection systems were also identified. For each of the four cases the indicators were calculated and analyzed to determine which one was the best system. The best values were obtained from the system with separation in five fractions: paper/cardboard, glass and lightweight packaging at drop-off points, organic waste and mixed waste in kerbside bins. Two regression models (linear and exponential) were developed in systems with enough data to explain and predict the variation in the amounts of materials that were separated correctly into containers, depending on the distance between containers and citizens. Nevertheless, the percentages of separation at source of paper/cardboard and lightweight packaging are still far from reaching the targets set by law.
Incorporation of ceria particles into the Ni matrix was found to improve the corrosion resistance of pure Ni coatings. With the aim of further improving the corrosion resistance of Ni-ceria, yttria was doped with ceria and used as distributed phase. About 8-mol% yttria doped ceria (8YDC) particles synthesized by a solution combustion process were dispersed in a nickel sulfamate bath and electrodeposition was carried out to prepare Ni–8YDC composite coatings at various current densities. The microhardness of the composite coatings was determined. Optical microscopy confirmed the incorporation of 8YDC particles into the Ni matrix. Potentiodynamic polarization and electrochemical impedance spectroscopy were used to characterize the corrosion behavior of the Ni–8YDC coatings. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Analysis (EDAX) were used to characterize the corroded samples. The results were compared with those for Ni and Ni–CeO2 coatings. The wear behavior of Ni–8YDC was studied. Wear tracks were characterized by MicroRaman Spectroscopy.
A variety of biomimetic stimuli-responsive soft grippers that can be utilized as intelligent actuators, sensors, or biomedical tools have been developed. This review covers stimuli-responsive materials, fabrication methods, and applications of soft grippers. This review specifically describes the current research progress in stimuli-responsive grippers composed of N-isopropylacrylamide hydrogel, thermal and light-responding liquid crystalline and/or pneumatic-driven shape-morphing elastomers. Furthermore, this article provides a brief overview of high-throughput assembly methods, such as photolithography and direct printing approaches, to create stimuli-responsive soft grippers. This review primarily focuses on stimuli-responsive soft gripping robots that can be utilized as tethered/untethered multiscale smart soft actuators, manipulators, or biomedical devices.
With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient as we move towards distributed and real-time processing. This paper is intended to aid the researcher or professional who understands machine learning but is inexperienced with big data. In order to evaluate tools, one should have a thorough understanding of what to look for. To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. We do this by starting from the beginning, and looking at what exactly the term “big data” means. From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together. We discuss the advantages and disadvantages of three different processing paradigms along with a comparison of engines that implement them, including MapReduce, Spark, Flink, Storm, and H2O. We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility. There is no single toolkit that truly embodies a one-size-fits-all solution, so this paper aims to help make decisions smoother by providing as much information as possible and quantifying what the tradeoffs will be. Additionally, throughout this paper, we review recent research in the field using these tools and talk about possible future directions for toolkit-based learning.
Hydrogen peroxide (H2O2) replaced oxygen (O2) as oxidant has been widely investigated due to its faster reduction kinetics, easier storage and handling than gaseous oxygen. The main challenge of using H2O2 as oxidant is the chemical decomposition. In this article, by using different C2H5OH/H2O volume ratio as the solvent, Co3O4 with different morphologies (nanosheet, nanowire, ultrafine nanowire net, nanobelts, and honeycomb-like) direct growth on Ni foam are synthesized via a simple solvothermal method for the first time. Results show that the introduction of ethanol could obviously improve the catalytic performance toward H2O2 electroreduction. The sample prepared in the solution with the C2H5OH/H2O volume ratio of 1:2 shows the best catalytic performance among the five samples and a current density of 0.214 A cm−2 is observed in 3.0 mol L−1 KOH + 0.5 mol L−1 H2O2 at −0.4 V (vs. Ag/AgCl KCl), which is much larger than that on the other metal oxides reported previously, almost comparable with the precious metals. This electrode of Co3O4 directly grown on Ni foam has superior mass transport property, which combining with its low-cost and facile preparation, make it a promising electrode for fuel cell using H2O2 as the oxidant.
In this study, we target to find a new composition for a layered mixed metal oxide, which has a high structural stability and a good electrochemical performance. Our strategy is to alter the transition metal composition focusing on the relative amounts of redox active Ni and Co to the inactive Mn, based on highly-stabilized LiNi1/3Co1/3Mn1/3O2. X-ray absorption near-edge structure and X-ray diffraction analyses show that the degree of cation disorder decreases on increasing the ratio of Ni and Co to Mn, by the presence of Ni3+, suggesting that slightly higher Ni and Co contents lead to improved structural stability. Electrochemical studies demonstrate that LiNi0.4Co0.4Mn0.2O2 cathodes exhibit considerable improvements in both the reversible capacity and the rate capabilities at a voltage range of 2.5–4.6 V. In situ XRD measurements reveal that LiNi0.4Co0.4Mn0.2O2 maintains a single-phase and undergoes lesser structural variations compared to controlled compositions during a delithiation process up to 4.6 V, while achieving a high reversible capacity over 200 mAh g−1. As a result, LiNi0.4Co0.4Mn0.2O2 experiences fewer structural degradations during electrochemical cycling, which explains the excellent long-term cycling performance.
We demonstrate exceptional performance for steam electrolysis at intermediate temperatures (500–650 °C) using protonic ceramic electrolyte cells comprised of the proton-permeable, high-activity mixed conductor PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) as the air electrode, the highly proton-conductive and chemically stable perovskite oxide BaZr0.4Ce0.4Y0.1Yb0.1O3 (BZCYYb4411) as the electrolyte, and a composite of Ni–BZCYYb4411 as the fuel electrode. Cells constructed from this material set have been shown previously to function efficiently in fuel cell mode. We demonstrate here reversible operation, enabling hydrogen production when excess electricity is available and immediate electricity generation from stored hydrogen when power demand is high. The cells are stable under cyclic operation and also under prolonged continuous operation in electrolysis mode, undergoing minimal loss in electrochemical characteristics after 500 h at 550 °C. Microstructurally optimized cells yield a remarkable current density of −1.80 A cm−2 at 600 °C and an operating voltage of 1.3 V, of which, based on an electrochemically deduced faradaic efficiency of 76%, −1.37 A cm−2 contributes to useful hydrogen.
Improving the cycle life and reliability of a battery is an important issue in lithium-ion battery (LIB) applications. Except for the material properties of the battery electrodes, the morphological features of LIBs also have a great influence on battery performance. In order to identify the relation between the morphological features of the electrodes and the macroscopic battery performance, a two-dimensional (2D) lattice Boltzmann model of ion and electron transport within LIB porous electrodes is presented in this study. The proposed model is superior to previous finite element method (FEM)-based models by providing a more convenient geometry generation process and a more efficient calculation technique. In the simulation, the lattice Boltzmann method (LBM) is utilized to solve the governing equations for ion and electron transport. The quartet structure generation set (QSGS) is employed to generate the electrode geometry with circular particles. The effects of the electrode micro-structure on the local concentration distribution, electric potential, and macroscopic discharge performance are investigated. Results show that the LBM is an optional approach in solving problems related to mass transport and electrochemical reactions. For the electrode particles, the obvious variations in local lithium concentration and electric potential prove that the electrode microstructure can influence the microscopic lithium transport; specifically, the lithium exchange is improved for smaller particle sizes. As for discharge performance, larger discharge depth can be achieved by a smaller cathode particle size and a larger cathode porosity. Meanwhile, a larger anode particle size and a smaller anode porosity both contribute to a larger discharge depth.
The identification of the Huntington's disease (HD) mutation as a CAG/polyglutamine repeat expansion enabled the generation of transgenic rodent models and gene-targeted mouse models of HD. Of these, mice that are transgenic for an N-terminal huntingtin fragment have been used most extensively because they develop phenotypes with relatively early ages of onset and rapid disease progression. Although the fragment models have led to novel insights into the pathophysiology of HD, it is important that models expressing a mutant version of the full-length protein are analysed in parallel. We have generated congenic C57BL/6 and CBA strains for the HdhQ150 knock-in mouse model of HD so that homozygotes can be analysed on an F1 hybrid background. Although a significant impairment in grip strength could be detected from a very early age, the performance of these mice in the quantitative behavioural tests most frequently used in preclinical efficacy trials indicates that they are unlikely to be useful for preclinical screening using a battery of conventional tests. However, at 22 months of age, the Hdh Q150/Q150 homozygotes showed unexpected widespread aggregate deposition throughout the brain, transcriptional dysregulation in the striatum and cerebellum and decreased levels of specific chaperones, all well-characterised molecular phenotypes present in R6/2 mice aged 12 weeks. Therefore, when strain background and CAG repeat length are controlled for, the knock-in and fragment models develop comparable phenotypes. This supports the continued use of the more high-throughput fragment models to identify mechanisms of pathogenesis and for preclinical screening.
Solar photovoltaic (PV) has the potential to make an important contribution to global sustainability, however, the misalignment between solar production and residential demand presents challenges for widespread PV adoption. Combining PV and storage is one way that this challenge can be overcome. In this work, we use one year of smart meter data from 369 consumers in three different US regions and calculate their economic benefits from both PV and coupled PV-battery systems. We consider a range of different electricity pricing schemes from the consumer regions, including both Feed-In-Tariff (FIT) and Net-Energy-Metering (NEM) policies. Significantly, our work uses real demand data, real PV generation data and optimizes each individual consumer’s battery operation to minimize their electricity bill. Furthermore, we study the effect of batteries on consumer self-sufficiency, which is important because increasing self-sufficiency is a primary motivating factor behind battery adoption. We find that PV is profitable for the majority of consumers with most current pricing scenarios but PV-battery systems are always less profitable. However, batteries can provide very significant increases in self-sufficiency and we find that a majority of consumers can exceed 70% self-sufficiency with a 20 kW h battery and a PV system that produces the equivalent of their consumption. This is compared to an average self-sufficiency of 35% with PV only. Finally, recognizing that a number of factors could lead to profitable batteries in future, we study the sensitivity of battery profitability to future electricity prices in a FIT scenario, also accounting for future decreases in PV and battery costs. We find that if PV-battery systems are to become better investments than PV-only for the majority of consumers, retail electricity prices above $0.40/kW h and FIT rates below $0.05/kW h are a likely requirement.
Lithium reduction at a graphite electrode in molten lithium chloride was studied at temperatures from 650 to 900°C using cyclic voltammetry and chronoamperometry. It was found that, during cathodic polarization, lithium intercalation into graphite occurred before deposition of metallic lithium started. This process was confirmed to be rate-controlled by the diffusion of lithium in the graphite. When the cathodic polarization potential was more negative than that for metallic lithium deposition, exfoliation of graphite particles from the electrode surface was observed. This was caused by fast and excessive accumulation of lithium intercalated into the graphite, which produced mechanical stress too high for the graphite matrix to accommodate. The erosion process was abated once the graphite surface was covered by a continuous layer of liquid lithium. These results are of relevance to the mechanism of carbon nanotube and nanoparticle formation by electrochemical synthesis in molten lithium chloride.
More than 4 decades ago, Sociologist Daniel Bell in “The Coming of Post-industrial Society” [1] attempted to draw people’s attention to the impending transit of the United States’ from a smoke-stack, assembly line-industrial economy to something different -- quite different, a knowledge intensive creative economy, where entirely different skill-sets than those the ‘educational factories’ were dispensing to “future workforces” would be needed. Fundamentally, Information and Communications Technology (ICT) tool-chest dominated ‘knowledge creation’ economies were going to present different societal challenges.
Modeling time-varying operations in complex energy systems optimization problems is often computationally intractable, and time-series input data are thus often aggregated to representative periods. In this work, we introduce a framework for using clustering methods for this purpose, and we compare both conventionally-used methods (k-means, k-medoids, and hierarchical clustering), and shape-based clustering methods (dynamic time warping barycenter averaging and k-shape). We compare these methods in the domain of the objective function of two example operational optimization problems: battery charge/discharge optimization and gas turbine scheduling, which exhibit characteristics of complex optimization problems. We show that centroid-based clustering methods represent the operational part of the optimization problem more predictably than medoid-based approaches but are biased in objective function estimate. On certain problems that exploit intra-daily variability, such as battery scheduling, we show that k-shape improves performance significantly over conventionally-used clustering methods. Comparing all locally-converged solutions of the clustering methods, we show that a better representation in terms of clustering measure is not necessarily better in terms of objective function value of the optimization problem.
Smoking prevalence is highly elevated in schizophrenia compared to the general population and to other psychiatric populations. Evidence suggests that smoking may lead to improvements of schizophrenia-associated attention deficits; however, large-scale studies on this important issue are scarce. We examined whether sustained, selective, and executive attention processes are differentially modulated by long-term nicotine consumption in 104 schizophrenia patients and 104 carefully matched healthy controls. A significant interaction of ‘smoking status’ × ‘diagnostic group’ was obtained for the domain of selective attention. Smoking was significantly associated with a detrimental conflict effect in controls, while the opposite effect was revealed for schizophrenia patients. Likewise, a positive correlation between a cumulative measure of nicotine consumption and conflict effect in controls and a negative correlation in patients were found. These results provide evidence for specific directional effects of smoking on conflict processing that critically dissociate with diagnosis. The data supports the self-medication hypothesis of smoking in schizophrenia and suggests selective attention as a specific cognitive domain targeted by nicotine consumption. A potential mechanistic model explaining these findings is discussed.
To date, many traditional Solar Home Systems (SHS) have consisted of separate components which required assembly by trained individuals and were also more susceptible to failure and maintenance. As a result, many SHSs in remote areas have not fulfilled their desired lifecycles or simply have not functioned at all. Thankfully, a solution to these problems has arrived—the newly developed Integrated Solar Home System (I-SHS). Within this new system all components such as the support structure, foundation, PV modules, charge controller, DC–AC converter and wiring are pre-assembled by the manufacturer. Benefits of the new system are ease of assembly and maintenance combined with an associated reduction in cost and failure—critical aspects to consider for remote and impoverished regions. Additionally, electrical yield was increased by 9% by a significant reduction of operating cell temperature. This was achieved by an integrated water tank, serving as a cooling unit and also providing the system’s foundation. This measure is neither expensive nor energy intensive, improves output of the system in an unproblematic way and allows for use of the heated water.
Sea turtles are highly susceptible to plastic ingestion and entanglement. Beach debris were surveyed along the most important sea turtle nesting beaches in Brazil (Costa dos Coqueiros, Bahia State). No significant differences among developed and undeveloped beaches were observed in terms of total number of items. Local sources (tourism activities) represented 70% of debris on developed beaches, where cigarette butts, straws, paper fragments, soft plastic fragments, and food packaging were the most abundant items. Non-local sources (domestic and fishing activities) accounted for about 70% of debris on undeveloped beaches, where the most abundant items were rigid plastic fragments, ropes, soft plastic fragments, caps, and polystyrene. The projected surface area of beach debris did not vary among developed and undeveloped beaches. Overseas containers accounted for about 25% of regional plastic pollution, implying that international pollution prevention agreements are not being respected off the Brazilian coast.