1,903 research outputs found

    Optical limiting using Laguerre-Gaussian beams

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    We demonstrate optical limiting using the self-lensing effect of a higher-order Laguerre-Gaussian beam in a thin dye-doped polymer sample, which we find is consistent with our model using Gaussian decomposition. The peak phase shift in the sample required for limiting is smaller than for a fundamental Gaussian beam with the added flexibility that the nonlinear medium can be placed either in front of or behind the beam focus.Comment: 3 pages, 4 figure

    Sea Coral-like NiCo2O4@(Ni, Co)OOH Heterojunctions for Enhancing Overall Water-Splitting

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    It is highly challenging to develop efficient and low-cost catalysts to meet stringent requirements on high current density for industrial water electrolysis application. We developed sea coral-like NiCo2O4@(Ni, Co)OOH heterojunctions, synthesized based on an epitaxial in-grown method using poly(ethylene glycol) (PEG) as a template, and explored its as efficient electrocatalyst for water-splitting. A two-electrode based alkaline electrolyzer was fabricated using NiCo2O4@(Ni, Co)OOH|| NiCo2O4@(Ni, Co)OOH, which achieved a current density value of 100 mA.cm−2 with a low potential of 1.83 V and high current density approached 600 mA.cm−2 at potential of 2.1 V along with a strong stability. These are superior to most reported data for the electrocatalysts operated at high current densities. In-situ calculations based on density function theory reveal that the occurrence of water-splitting on the NiCo2O4@(Ni, Co)OOH heterojunction surface. First-principles molecular dynamics simulation reveals that the stretching vibrations of metallic bonds of NiCo2O4@(Ni, Co)OOH heterojunctions open the hydrogen bonds of water. Understanding the mechanism of water-splitting at the heterojunction from in-situ theoretical calculations is helpful to develop new generation industrial catalysts

    The impact of cognitive training on cerebral white matter in community-dwelling elderly : one-year prospective longitudinal diffusion tensor imaging study

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    It has been shown that cognitive training (CogTr) is effective and recuperative for older adults, and can be used to fight against cognitive decline. In this study, we investigated whether behavioural gains from CogTr would extend to white matter (WM) microstructure, and whether training-induced changes in WM integrity would be associated with improvements in cognitive function, using diffusion tensor imaging (DTI). 48 healthy community elderly were either assigned to multi-domain or single-domain CogTr groups to receive 24 sessions over 12 weeks, or to a control group. DTI was performed at both baseline and 12-month follow-up. Positive effects of multi-domain CogTr on long-term changes in DTI indices were found in posterior parietal WM. Participants in the multi-domain group showed a trend of long-term decrease in axial diffusivity (AD) without significant change in fractional anisotropy (FA), mean diffusivity (MD) or radial diffusivity (RD), while those in the control group displayed a significant FA decrease, and an increase in MD and RD. In addition, significant relationships between an improvement in processing speed and changes in RD, MD and AD were found in the multi-domain group. These findings support the hypothesis that plasticity of WM can be modified by CogTr, even in late adulthood

    Penerapan Pendekatan Pengajaran Terbalik (Reciprocal Teaching) Untuk Meningkatkan Kemandirian Belajar Biologi Siswa Kelas Vii-g SMP N 5 Karanganyar Tahun Pelajaran 2010/ 2011

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    – The objective of this study is to improve student independence in learning biology by implementing Inverted Teaching Approach (Reciprocal Teaching) on Environmental Management material. This research is a classroom action research. This research was conducted in two cycles. Each cycle consisted of planning, implementation of the action,observation, and reflection. The subjects of the study were VII-G class students of SMP Negeri 5 Karanganyar in the academic year of 2010/2011. The number of the students was 32. The technique and instrumen of collectiing data were questionnaire, observation, and interviews. The technique of analyzing data was descriptive analysis techniques. Triangulation technique was used in data validation. The results proved that by implementing Inverted Teaching Approach (Reciprocal Teaching) students\u27 independence in learning biology enhanced. It is based on the results of questionnaires, observations and interviews. The questionnaire of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 67.97%, 72.55%, and 77.58% respectively. The observation of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 39.68%, 67.5%, and 80.62% respectively. It can be concluded that the implementation of Inverted Teaching Approach (Reciprocal Teaching) can enhance students learning independence

    The Influence of Anthropomorphic Chatbot Design on Consumer Tolerance of Service Failures: The Mediating Roles of Attachment and Cognitive Dissonance

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    Problem statement: The widespread use of chatbots in hospitality and tourism leads to inevitable service failures. Although research has investigated the influence of chatbots` anthropomorphic cues, comprehending how distinct anthropomorphic cues influence user behavior in service failure is still limited. Methodology: To explore how the anthropomorphic design of chatbots affect user`s tolerance for service failure, this research conducts a 2 (anthropomorphic appearance: 3D vs. 2D) x 2 (language style: informal vs. formal) x 2 (interdependent self-construal: high vs. low) between-subject online experiment. Result: Results show that the congruent anthropomorphic cues of chatbots can significantly improve consumers \u27 tolerance, where attachment mediates this process positively. Additionally, the interdependent self-construal level plays a positive moderating role in this process. Implications: This study contributes theoretically by explicating anthropomorphism in attachment and cognitive dissonance theory and extending the understanding of self-construal theory. Moreover, the study provides recommendations for managers to design effective anthropopathic chatbots

    Exploration of the Cultivation Model for the Vocational Competence of Full-time Professional Master's Degree Postgraduates

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    In line with the current national demand for high-level innovative, applied, and composite professionals in the new era, and based on the problems and challenges faced in the cultivation of the vocational competence of professional master’s degree postgraduates, such as unclear cultivation methods, lack of systematic practice, and incomplete integration of industry and education, this paper proposes, with a focus on enhancing vocational competence, to construct a “one-body-two-wings” cultivation model for vocational competence. In this model, “professional ability” serves as the body, while “theoretical knowledge + research ability” acts as the two wings

    Combination of Fault Tree and Neural Networks in Excavator Diagnosis

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    By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA) and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.233
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