batterydata/paper-abstracts huggingface dataset:
Better knowledge of food search behaviour in fish is essential for studies that aim to improve longline fishing, particularly through bait development. This review provides an overview of our understanding of how fish detect and locate sources of food odour, focusing on the stimuli and sensory modalities involved, and on factors that affect feeding activity. Studies that identify feeding attractants and efforts to develop alternative longline baits are presented. The review reveals that such studies are few in number, and that to date there are no alternatives to traditional baits in commercial longlining despite the growing demand for these resources, which are also used for human consumption. The chemical compounds that elicit food search behaviour differ from species to species, and species selectivity could be improved by incorporating specific feeding attractants in manufactured baits. The unique properties of chemical stimuli and odour dispersal form the basis for improving longline efficiency through the development of a long-lasting bait. Vision is important in prey capture, and manufactured baits can be made more visible than natural baits by increasing the contrast (e.g. via fluorescent or polarising coatings) and creating motion through buoyancy. Physical properties such as size, shape, texture and strength can also be manipulated in a manufactured bait to improve catch efficiency. Knowledge obtained from studies of various aspects of food search behaviour is of paramount importance for future research aimed at alternative bait development and improving longline fishing.
Studies reporting altered susceptibility to visual illusions in autistic individuals compared to that typically developing individuals have been taken to reflect differences in perception (e.g. reduced global processing), but could instead reflect differences in higher-level decision-making strategies.
Selenium nanoparticles are encapsulated uniformly with reduced graphene oxide (Se@RGO) by a simple self-assembly process. The Se@RGO composite has as high as 80 wt% of Se, which is much larger than that of other selenium-based cathode materials. The Se@RGO cathode delivers high capacity and rate capability in a Li–Se battery because of the large capacity of Se, highly dispersed Se nanoparticles in the RGO framework and high conductivity of Se and RGO nanocomposite cathode. A large initial discharge capacity of 533 mAh g−1 at 0.2C was produced based on the total mass of Se@RGO nanocomposites and the capacity could remain at 265 mAh g−1 after 200 cycles at 0.2C and additional 300 cycles at 1C. The electrode also shows an excellent capacity of 250 mAh g−1 at 5C (∼3.4 A g−1). The superior electrochemical performance suggests that the Se@RGO composite is a promising cathode in high-energy and high-power Li–Se battery.
Hybrid hollow polymeric microspheres (HPMSs) are synthesized by encapsulating the supramolecular vesicles with polyphosphazene through a rapid one-step polycondensation reaction. Subsequent carbonization treatments of the HPMSs lead to corresponding hollow carbon microspheres (HCMSs) with well-preserved geometry. The sizes of HPMSs and HCMSs are controlled by the vesicles, which is directly determined by the feeding ratio of the assembly units. Electrodes based on HCMSs showed a specific capacitance of 314.6F/g at a current density of 0.2A/g in 6M KOH electrolyte, 180.0F/g at a current density of 30A/g, and high stability of 98.2% of capacity retention after 2000 cycles. Both the high surface area and high heteroatoms level of HCMSs contribute to the excellent capacitive performance. Meanwhile, the hollow carbon structure ensured the satisfactory capacitive performance by increasing utilization efficiency of the surface area as well as achieving short diffusion paths for electrolyte ions.
Résumé Introduction La cranioplastie après craniectomie décompressive chez le traumatisé crânien sévère permet souvent d’améliorer l’état clinique de ces patients, sans que la physiopathologie exacte ne soit totalement expliquée. L’amélioration de l’hémodynamique et du métabolisme cérébral locaux pourraient être une explication. Nous avons évalué l’impact de la cranioplastie sur l’évolution clinique des traumatisés crâniens sévères et sur l’évolution de la perfusion cérébrale. Patients et méthodes Cette étude multicentrique et prospective a concerné 24 patients traumatisés crâniens graves ayant eu une cranioplastie dans les 12 semaines suivant la craniectomie décompressive. Une évaluation neurologique et cognitive, et de la perfusion cérébrale par scanner de perfusion et doppler transcrânien a été réalisée avant et après cranioplastie. Résultats Une amélioration neurologique et cognitive significative a été observée chez 92 % des patients à six mois de la cranioplastie. La perfusion cérébrale était globalement améliorée dès six semaines postopératoires prédominant du côté de la cranioplastie. Les vitesses de circulation sanguine systoliques et diastoliques étaient augmentées dans les deux artères cérébrales moyennes. Conclusion La cranioplastie après craniectomie décompressive semble améliorer le pronostic fonctionnel des traumatisés crâniens sévères via une amélioration globale de la perfusion cérébrale.
Social cognition has received increasing attention in schizophrenia due to its theoretical relevance to core features of the disorder as well as the marked deficits in social functioning exhibited by these patients. However, there remains a need to develop and validate measures of social cognitive abilities and to demonstrate that they are constructs that are separable from non-social neurocognitive processes. In the current study, the Wechsler Adult Intelligence Scale-Revised (WAIS-R) was administered to 169 males with schizophrenia, and test results were subjected to confirmatory factor analysis (CFA) to determine if those WAIS-R subtests containing social content would form a distinct Social Cognition (SC) factor. CFA was used to evaluate various models that hypothesized an SC factor, and for comparison purposes the same models were evaluated in the WAIS-R standardization sample. Results confirmed the presence of a four-factor model that included an SC factor, as well as the more commonly reported Verbal Comprehension, Perceptual Organization, and Working Memory factors. The SC factor consisted of the Picture Arrangement and Picture Completion subtests, and demonstrated small but significant correlations with disorganization and negative symptoms, as well as with an index of social functioning. Results provide support for the validity of the SC factor as a measure of social cognition in schizophrenia, and demonstrate that at least some aspects of social cognition represent separable cognitive domains in schizophrenia.
An explicit model predictive control (EMPC) system for a hybrid battery-ultracapacitor power source is proposed and experimentally verified in this paper. The main advantage of using the EMPC system is that the control law computation is reduced to evaluation of an explicitly defined piecewise linear function of the states. Separate EMPC systems for the total output current loop, the battery loop and the ultracapacitor loop are designed. This modular design approach allows evaluation of the performance of each individual EMPC system separately and also improves the convergence of the EMPC system design algorithm as the models used to design each loop are smaller. In order to protect the hybrid power source, the designed EMPC systems maintain operation of the hybrid power source within specified constraints, namely, battery and ultracapacitor current constraints, battery state of charge constraints and ultracapacitor voltage constraints. At the same time, the total output current EMPC system allocates high frequency current changes to the ultracapacitor and the low frequency current changes to the battery thus extending the battery lifetime. Presented experimental results verify that the hybrid power source operates within the specified constraints while allocating high and low frequency current changes to the ultracapacitor and battery respectively.
This paper proposes a high performance single-stage inverter topology for the autonomous operation of a solar photovoltaic system. The proposed configuration which can boost the low voltage of photovoltaic (PV) array, can also convert the solar dc power into high quality ac power for driving autonomous loads without any filter. An MPPT circuit with parallel connection is implemented so that the part of the energy generated is processed by the dc–dc converter to supply dc loads. The line current total harmonic distortion (THD) obtained using this configuration is quite reasonable. The proposed topology has several desirable features such as low cost and compact size as number of switches used, are limited to four as against six switches used in classical two-stage inverters. In this paper analysis, simulation and experimental results are presented.
Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient’s smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.
The thermal management of a cylindrical battery cell by a phase change material (PCM)/compressed expanded natural graphite (CENG) is investigated in this study. The transient thermal behaviour of both the battery and the PCM/CENG is described with a simplified one-dimensional model taking into account the physical and phase change properties of the PCM/CENG composite. The 1D analytical/computational model yielded nearly identical results to the three-dimensional simulation results for various cooling strategies. Therefore, the 1D model is sufficient to describe the transient behaviour of the battery cooled by a PCM/CENG composite. Moreover, the maximum temperature reached by the PCM/CENG cooling strategy is much lower than that by the forced convection in the same configuration. In the test case studied, the PCM showed superior transient characteristics to forced convection cooling. The PCM cooling is able to maintain a lower maximum temperature during the melting process and to extend the transient time for temperature rise. Furthermore, the graphite-matrix bulk density is identified as an important parameter for optimising the PCM/CENG cooling strategy.
Cd(OH)2 nanowires have been synthesized on nickel foam by a simple template-free growth method. Structural characterization by SEM and TEM indicated that the Cd(OH)2 formed a porous film on the surface of the nickel foam skeletons. The thickness of the film reached approximately 25μm and the nanowires have diameters of around ∼100nm. The nanowires is confirmed to be pure phase hexagonal Cd(OH)2 by X-ray diffraction. The electrochemical capacitance behaviors of the Cd(OH)2 nanowires electrode are investigated by cyclic voltammetry, galvanostatic charge/discharge and electrochemical impedance spectroscopy tests. The Cd(OH)2 electrode performed in different electrolyte demonstrates that nanowires possess supercapacitance properties in the presence of high concentration OH− ion. The specific capacitances as high as 1164.8Fg−1 at 1Ag−1 and 257.6Fg−1 at 10Ag−1 are obtained in 6moldm−3 KOH solution. The remarkably high capacitance of the Cd(OH)2 nanowire electrode might be attributed to its unique 3D open structure for easy access of electrolyte ions, large surface area and high electrochemical activity. This work demonstrates that Cd(OH)2 nanowires electrode has potential application in electrochemical capacitors.
Aluminum-ion batteries, emerging as a promising post-lithium battery solution, have been a subject of increasing research interest. Yet, most existing aluminum-ion research has focused on electrode materials development and synthesis. There has been a lack of fundamental understanding of the electrode processes and thus theoretical guidelines for electrode materials selection and design. In this study, by using density functional theory, we for the first time report a first-principles investigation on the thermodynamic and kinetic properties of aluminum intercalation into two common TiO2 polymorphs, i.e., anatase and rutile. After examining the aluminum intercalation sites, intercalation voltages, storage capacities and aluminum diffusion paths in both cases, we demonstrate that the stable aluminum intercalation site locates at the center of the O6 octahedral for TiO2 rutile and off center for TiO2 anatase. The maximum achievable Al/Ti ratios for rutile and anatase are 0.34375 and 0.36111, respectively. Although rutile is found to have an aluminum storage capacity slightly higher than anatase, the theoretical specific energy of rutile can reach 20.90 Wh kg−1, nearly twice as high as anatase (9.84 Wh kg−1). Moreover, the diffusion coefficient of aluminum ions in rutile is 10−9 cm2 s−1, significantly higher than that in anatase (10−20 cm2 s−1). In this regard, TiO2 rutile appears to be a better candidate than anatase as an electrode material for aluminum-ion batteries.
We investigated how preschoolers use their understanding of the actions available to a speaker to resolve referential ambiguity. In this study, 58 3- and 4-year-olds were presented with arrays of eight objects in a toy house and were instructed to retrieve various objects from the display. The trials varied in terms of whether the speaker’s hands were empty or full when she requested an object as well as whether the request was ambiguous (i.e., more than one potential referent) or unambiguous (i.e., only one potential referent). Results demonstrated that both 3- and 4-year-olds were sensitive to speaker action constraints and used this information to guide on-line processing (as indexed by eye gaze measures) and to make explicit referential decisions.
A three-dimensional transient model is developed for a non-aqueous-electrolyte lithium-air battery to investigate numerically key phenomena during discharging. The proposed model rigorously assesses lithium peroxide (Li2O2) formation and growth in an air electrode, and its complex interactions with electrochemical reactions and species/charge transport. We assume that the porous air electrode mainly consists of sphere-like carbon particles, and hence a spherical film growth model is adopted to simulate the precipitation of Li2O2 on the spherical carbon surfaces. The model is first validated against voltage evolution data measured at different discharging current densities. Good agreement between the simulation results and experimental data is achieved, demonstrating that the model accurately captures voltage decline behaviors due to accumulated Li2O2 in the air electrode. Additional simulations are carried out with different air electrode designs in order to establish the optimization strategy for the air electrode. Detailed simulation results, including multidimensional contours, clearly indicate that the electrode thickness and degree of electrolyte filling are key factors for improving the discharging performance of lithium-air batteries.
Background Functional cerebral asymmetries (FCAs) are known to fluctuate across the menstrual cycle. The visual line-bisection task administered to normally cycling women showed different patterns of the interhemispheric interactions during menses and the midluteal cycle phase. However, the contribution of estrogens and progestins hormones to this phenomenon is still unclear. Study Design The aim of our study was to show a variation of FCAs in women administered oral contraceptives (OCs) using the visual line-bisection task. Visual line-bisection task with three horizontal lines was administered to 36 healthy women taking a 21-day OC. Twenty-nine patients were right handed. The task was administered during OC intake (day 10) and at the end of the pill-free period. Results The right-handed women showed a significant leftward bias of veridical center on the first and third lines during OC intake compared with an opposite rightward bias during the pill-free period. The same phenomenon of contralateral deviation was observed in left-handed women on day 10 of OC intake. Conclusions The results of this study confirm a hormonal modulation on interhemispheric interaction and suggest that OCs may improve the interhemispheric interaction reducing FCAs compared with the low hormone level period. This opens new insights in OC prescription and choice of administration schedule in order to improve cognitive performances.
A new catalyst electrode was prepared in which Pt particles were homogeneously distributed into a poly(3,4-ethylenedioxythiophene)–poly(styrene sulfonic acid) (PEDOT–PSS) matrix. Catalytic activity and stability for the oxidation of methanol were studied by using cyclic voltammetry and chronoamperometry. For comparative purposes, bulk Pt and PEDOT–PSS based electrodes were fabricated and tested. Enhanced electrocatalytic activity toward the oxidation of methanol was noticed when Pt particles were embedded into the PEDOT–PSS matrix. A high catalytic current for methanol oxidation (2.51mAcm−2) was found for the PEDOT–PSS–Pt electrode in comparison to bulk Pt electrode (0.45mAcm−2) at +0.6V (versus Ag/AgCl). The enhanced electrocatalytic activity might be due to the dispersion of Pt particles into the PEDOT–PSS matrix and the synergistic effects between the dispersed Pt particles and the PEDOT–PSS matrix. The morphology and crystalline behavior of PEDOT–PSS–Pt and simple ITO/Pt films were determined by X-ray diffraction (XRD) analysis and scanning electron microscopy (SEM) and correlated with the enhanced electrocatalytic activity for the Pt-dispersed PEDOT–PSS electrode.
The effect of δ-Al2O3 nanoparticles (size∼10nm) on the ionic conductivity and related properties of a solid polymer electrolyte (SPE) that consists of low molecular weight (M w=2000) poly(ethylene) glycol (PEG) complexed with LiClO4 is studied in detail using XRD, DSC, TPD, NMR, and complex impedance methods. The maximum ionic conductivity σ=7.3×10−7 Scm−1 observed for pristine (PEG)46LiClO4 is found to be dependent on the content of δ-Al2O3 filter; it shows a peak value of σ=4.5×10−6 Scm−1 for 10mol% filler. This is nearly an order of magnitude enhancement of the ionic conductivity and is found by DSC studies to be related to a decrease in the crystalline regions in the SPE, while the glass transition temperature T g and the melting temperature T m remain essentially unchanged. 7Li NMR motional narrowing points to an increase in the effective mobility of the lithium ions on doping with the nanoparticles. The temperature dependence of σ can be divided into two regions, one consistent with the Arrhenius behavior and the other with the Vogel, Tamman and Fulcher equation. The activation energy is found to be the lowest for the 10mol% doped sample. It is concluded that doping with nanoparticles leads to an enhancement of conductivity due to a decrease in the crystallinity and the activation energy, as well as to an increase in the effective mobility of Li ions.
The study of errors in learning and the search for patterns to explain their causes have always been of great interest to researchers and educators alike. Mistakes are a constant in students’ solutions to mathematical problems and are inseparable from the learning process. It is essential, then, to diagnose and address the mistakes made by students so as to allow them to reflect on their errors and adjust their knowledge. To this end, we have created a system that tracks all the actions carried out by a student when solving a mathematical algorithm, not just the final results, and which is capable of diagnosing the faults and possible causes. It can also recommend the actions to be taken based on the individual difficulties encountered. In short, we have created a personalized teaching system whose features could be particularly useful for special-needs students, such as those with Down syndrome. This paper explains the error detection modules in the addition, subtraction and error-adapted assistance algorithms. This work is part of a multidisciplinary research effort financed by R&D project called “Divermates”, of the Ministry of Labor and Social Affairs, and involving personnel from the Computer Engineering and Mathematics and Fine Arts Education Departments of the University of La Laguna, as well as professionals from the Tenerife Trisomic 21 Association (ATT21).
Developing an electrode material with improved ionic transport dynamics in a battery has been the focus of research. Here, we report a facile one-step hydrothermal synthesis method to prepare anode material of ultra-small SnS nanocrystals (NCs) anchored on N-doped graphene nanosheets (SnS/N-G), which is expected to significantly the dynamics of lithium transport, enabling an exceptional capacity of 1120.3 mAh g−1 at 0.1 A g−1 after 130 cycles and superior rate capabilities of 446.3 and 340.7 mAh g−1 at 2 and 3 A g−1, respectively. Furthermore, the lithiation/delithiation behaviors of SnS/N-G anode were observed in real time using in situ transmission electron microscopy to reveal the corresponding kinetics. By tracking the full lithiation procedure, in situ electron diffraction and high-resolution TEM imaging found that the original SnS phase was firstly transformed to Sn phase by conversion reaction and then to Li22Sn5 phase by alloying reaction. Notably, a stable and reversible phase transformation was established between Li22Sn5 and Sn phases during subsequent charge-discharge cycles. In the meantime, the volume expansion-induced pulverization of SnS NCs was evidently alleviated by graphene matrix that not only provided a two-dimensional support to buffer the volume change, but also improved the ion migration kinetics, as corroborated by superior rate capability.
This paper describes a model identification procedure for identifying an electro-thermal model of lithium ion batteries used in automotive applications. The dynamic model structure adopted is based on an equivalent circuit model whose parameters are scheduled on the state-of-charge, temperature, and current direction. Linear spline functions are used as the functional form for the parametric dependence. The model identified in this way is valid inside a large range of temperatures and state-of-charge, so that the resulting model can be used for automotive applications such as on-board estimation of the state-of-charge and state-of-health. The model coefficients are identified using a multiple step genetic algorithm based optimization procedure designed for large scale optimization problems. The validity of the procedure is demonstrated experimentally for an A123 lithium ion iron-phosphate battery.