Doing Action Research: The Experience of Teachers in Seychelles

Action research is regarded as a dynamic strategy to galvanise teachers to determine what works best for them and their pupils. Teachers’ experience in action research has been investigated in some developing countries without involving any of the small island states in the Indian Ocean. Hence, this study explored the experience of teachers from Seychelles regarding action research focusing on their understanding of the nature, meaning, and purpose of action research; the benefits they gained from doing action research; the difficulties and the challenges they encountered while conducting action research, including their background characteristics. Its aim was to identify their successes, concerns, and issues. Participants were 33 primary school teachers enrolled in the two-year Advanced Diploma programme at the Seychelles Institute of Education during the 2019/2020 academic year. One of their assessment tasks required them to identify a difficulty that their pupils encounter and conduct action research on it with a view to finding a solution. Data collected using a self-reporting questionnaire designed by the investigators was analysed using both descriptive and interpretive techniques. Results indicated that the participants had a mixed experience of successes, concerns, and issues.

Multi-Criteria Analysis for the Prioritization of Areas for the In Situ Conservation of Crataegus L., an Underutilized Fruit Tree in Mexico

Complementary ex situ and in situ conservation, including the on-farm alternative, is a highly desired and dynamic strategy that allows the natural evolution of the conserved germplasm. Due to the high costs involved, in addition to the limitations of both economic and human resources, in situ conservation must focus on areas where the greatest benefits are obtained, and the efforts made result in better impacts. Therefore, using spatial multi-criteria analysis and expert knowledge, 22 and 23 criteria were obtained as important for the conservation of wild and cultivated hawthorn, respectively. Criteria weights were calculated by the analytic hierarchy process and expert knowledge. The results showed species richness, phenotypic and ecogeographic diversity, and areas not covered by the official protected areas network were the most important criteria for in situ conservation of wild hawthorn. Prioritized areas were particularly focused in Chiapas, State of Mexico and Morelos. The prioritized areas for the in situ conservation of cultivated hawthorn were mostly defined by criteria such as number of cultivated varieties, number of uses, phenotypic diversity, ecogeographical diversity, and areas with rainfed agriculture. These areas were located mainly in Puebla. From this study, we propose a list of priority areas for the in situ conservation of both cultivated and wild hawthorn.

Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.

Efficient Dynamic Computational Strategy for Heterogeneous Catalysis Based on Neural Network Potential Energy Surface: A Case Study of Temperature-Dependent Thermodynamics and Kinetics for the Chemisorbed on-surface CO

As a favorable alternative and complement of experimental techniques, computational tools on top of ab initio calculations have played an indispensable role in revealing the molecular details, thermodynamics and kinetics in catalytic reactions. The static computational strategy, which recovers the reaction thermodynamics and kinetics based on the calculations of a few stationary geometries at zero temperature and some ideal statistic mechanics models, is the most popular approach in theoretical catalysis due to its simplicity. In comparison, the ab initio molecular dynamics (AIMD) is a well-tested approach to provide more precise descriptions of catalytic processes, however, experiencing a significantly expensive computational cost in the direct ab initio calculation of potential energy and gradients. Here we proposed a highly efficient dynamic computational strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of neural network potential energy surface (NN PES) and MD simulations. Taking CO adsorbate on Ru(0001) surface as the illustrative model catalytic system, we demonstrated that our NN-PES-based MD simulations can efficiently generate the reliable smooth two-dimensional potential-of-mean-force (2-D PMF) surfaces in a wide range of temperatures (from 300 to 900 K), and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface rather than a rough estimation using ideal models based on a few optimized geometries. Moreover, MD simulations offer an effective way to describe the surface kinetics such as the CO adsorbate on-surface movement, which goes beyond the most popular static estimation based on calculated free energy barrier and transition state theory (TST). By comparing the results obtained in the dynamic and static approaches, we further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST. It is expected that this accurate yet efficient dynamic strategy can be a powerful tool in understanding reaction mechanisms and reactivity of a catalytic surface system, and further guides the rational design of heterogeneous catalysts.

Hedge Fund Manager Skill and Style-Shifting

Using a novel style identification procedure, we show that style-shifting is a dynamic strategy commonly used by hedge fund managers. Three quarters of hedge funds shifted their investment styles at least once over the period from January 1994 to December 2013. We perform empirical tests of two hypotheses for the motivations of hedge fund style-shifting, namely backward-looking and forward-looking hypotheses. We find no evidence that style-shifting funds are backward-looking. Instead, we show evidence that managers of style-shifting funds exhibit both style-timing ability and the skill of generating abnormal returns in new styles. The new styles that hedge funds shift to on average outperform their old styles by 0.76% and style-shifting funds on average outperform their new style benchmark by 1.10% over the subsequent 12-month horizon. Finally, we show that small funds, winner funds, and funds with net inflows are more likely to shift styles. This paper was accepted by David Simchi-Levi, finance.

Standard automated perimetry using size III and size V stimuli in advanced stage glaucoma: an observational cross-sectional comparative study

ObjectivesIn this study, we sought to evaluate the extent of further visual field that could be assessed when using stimulus size V in standard automated perimetry compared with size III in advanced stage glaucoma and whether cut-off values could be determined for when to switch from size III to size V.DesignProspective cross-sectional study.SettingSingle-centre outpatient eye clinic in India (New Delhi).ParticipantsAdvanced stage glaucoma defined as stages 3–4.InterventionCentral static perimetry with Octopus 900 G programme (size III stimulus dynamic strategy) and low vision central programme (size V stimulus dynamic strategy).Primary and secondary outcome measuresVisual field assessment for right and left eyes with both sizes III and V were undertaken within one clinic visit.ResultsWe recruited 126 patients (170 eyes). Mean patient age at assessment was 55.86 years (SD 15.15). Means (SD) for size III versus size V, respectively, were 6.94 dB (5.58) and 12.98 dB (7.77) for mean sensitivity, 20.02 dB (5.67) and 19.22 dB (7.74) for mean deviation, 5.89 dB (2.29) and 7.69 dB (2.78) for standard loss variance and 3.32 min (1.07) and 6.40 min (1.43) for test duration. All except mean deviation were significantly different between size III and V tests.ConclusionUseful visual field information was obtained with size V stimuli which allowed continued monitoring of these patients that was not possible with size III. Increased test duration, standard loss variance and mean sensitivity were found with size V, as expected, given that more visual responses were obtained with the increased target size. A switch from size III to V may be considered when mean sensitivity reaches 10 dB and/or mean deviation reaches 18 dB.

OPTIMAL CONTROL OF THE DECUMULATION OF A RETIREMENT PORTFOLIO WITH VARIABLE SPENDING AND DYNAMIC ASSET ALLOCATION

We extend the Annually Recalculated Virtual Annuity (ARVA) spending rule for retirement savings decumulation (Waring and Siegel (2015) Financial Analysts Journal, 71(1), 91–107) to include a cap and a floor on withdrawals. With a minimum withdrawal constraint, the ARVA strategy runs the risk of depleting the investment portfolio. We determine the dynamic asset allocation strategy which maximizes a weighted combination of expected total withdrawals (EW) and expected shortfall (ES), defined as the average of the worst 5% of the outcomes of real terminal wealth. We compare the performance of our dynamic strategy to simpler alternatives which maintain constant asset allocation weights over time accompanied by either our same modified ARVA spending rule or withdrawals that are constant over time in real terms. Tests are carried out using both a parametric model of historical asset returns as well as bootstrap resampling of historical data. Consistent with previous literature that has used different measures of reward and risk than EW and ES, we find that allowing some variability in withdrawals leads to large improvements in efficiency. However, unlike the prior literature, we also demonstrate that further significant enhancements are possible through incorporating a dynamic asset allocation strategy rather than simply keeping asset allocation weights constant throughout retirement.

ADVANCE ATTACK AND DEFENSE STRATEGY ALGORITHM WITH DYNAMIC ROLE ASSIGNMENT FOR WHEELED SOCCER ROBOT

Game strategy is one of the most critical parts of winning a soccer robot match and cannot be separated from the cooperation among robots in making movements to score goals. In this paper, a wheeled soccer robot game strategy called advance attack and defense has been developed. The strategy is combined with dynamic role assignment, in which robot can change from an attacker to a defender and vice versa. Defender robots are not only based on defensive area but will always block opposing attacker to score goal. The attack strategy performs a rotational trajectory for attacker robot to overpass opponent robot. This strategy has been proven to increase defense and attack effectiveness. Test results using soccer robot gameplay environment simulator developed by Institut Teknologi Sepuluh Nopember Robot with Intelligent System (IRIS) team show that the advance strategies are superior compared with basic strategies. In 30 matches, the advance dynamic strategy won 80%, drew 6.7%, and obtained the highest goal difference, 85 goals. The test was then verified with the implementation in the IRIS robots and showed the same performance. The developed game algorithms were tested in 2019 Indonesian wheeled soccer robot contest (KRSBI-B) and the IRIS team won the title.