The Effectiveness of English Writing Teaching in Junior Middle School Based on Production-Oriented Approach

Production-oriented Approach (POA) proposed by Chinese scholar Wen Qiufang has been widely used in English teaching in recent years, but there are few studies on its application in junior middle school English teaching. This study analyzed the impact of the application of Production-oriented Approach on junior middle school students’ English learning attitude and English writing performance. In the experimental design, both quantitative and qualitative methods were adopted. Writing tests, questionnaire, interview were used as instruments. A total of 116 Chinese students from Year 8 of Yangzhou Shiyan Junior Middle School, Jiangsu Province in China were invited to participate in an 8-week pre- and post-test experiment. By comparing the writing scores before and after the test, it is found that the English writing scores of the students in the experimental class are higher than those of the students in the control class. Through the analysis of the results of questionnaires and interview, it is found that the students’ attitude towards English writing teaching in the experimental class has improved significantly. The implications and suggestions for dissemination and implementation of POA for junior middle school students are discussed

On the equivalence between Value-at-Risk and Expected Shortfall in non-concave optimization

This paper studies an optimal asset allocation problem for a surplus-driven financial institution facing a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint corresponding to a non-concave optimization problem under constraints. We obtain the closed-form optimal wealth with the ES constraint as well as with the VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions. We find that both VaR and ES-based regulation can effectively reduce the probability of default for a surplus-driven financial institution. However, the liability holders' benefits cannot be fully protected under either VaR- or ES-based regulation. In addition, we show that the VaR and ES-based regulation can induce the same optimal portfolio choice for a surplus-driven financial institution. This differs from the conclusion drawn in Basak and Shapiro 2001 where the financial institution aims at maximizing the expected utility of the total assets, and ES provides better loss protection

Intergenerational risk sharing in a collective defined contribution pension system: a simulation study with Bayesian optimization

Pension reform is a crucial societal problem in many countries, and traditional pension schemes, such as Pay-As-You-Go and Defined-Benefit schemes, are being replaced by more sustainable ones. One challenge for a public pension system is the management of a systematic risk that affects all individuals in one generation (e.g., that caused by a worse economic situation). Such a risk cannot be diversified within one generation, but may be reduced by sharing with other (younger and/or older) generations, i.e., by intergenerational risk sharing (IRS). In this work, we investigate IRS in a Collective Defined-Contribution (CDC) pension system. We consider a CDC pension model with overlapping multiple generations, in which a funding-ratio-liked declaration rate is used as a means of IRS. We perform an extensive simulation study to investigate the mechanism of IRS. One of our main findings is that the IRS works particularly effectively for protecting pension participants in the worst scenarios of a tough financial market. Apart from these economic contributions, we make a simulation-methodological contribution for pension studies by employing Bayesian optimization, a modern machine learning approach to black-box optimization, in systematically searching for optimal parameters in our pension model

Spacings around and order statistic

We determine the joint limiting distribution of adjacent spacings around a central, intermediate, or an extreme order statistic Xk:n of a random sample of size n from a continuous distribution F. For central and intermediate cases, normalized spacings in the left and right neighborhoods are asymptotically i.i.d. exponential random variables. The associated independent Poisson arrival processes are independent of Xk:n. For an extreme Xk:n, the asymptotic independence property of spacings fails for F in the domain of attraction of Fréchet and Weibull (α≠1) distributions. This work also provides additional insight into the limiting distribution for the number of observations around Xk:n for all three cases

The slip surface mechanism of delayed failure of the Brumadinho tailings dam in 2019

The 2019 Feijão dam failure in Brumadinho, Brazil, claimed 270 lives and caused enormous environmental damage. A special feature of this failure was that it took place three years after the tailings disposal was terminated, which should have allowed sufficient time for the material to consolidate and increase its strength. Here we propose a basic physical mechanism of a delayed slip surface growth along weak layers of fine tailings within the dam body. Using accurate numerical modelling of all stages of the evolution of the Feijão dam, we show how this growth was preconditioned by dam construction and tailings discharge history and further driven by creep deformation during the post-closing stage, until the slip surfaces reached their critical length, resulting in their unstable propagation and the rapid collapse of the entire dam. Main factors controlling the time of failure have been identified, facilitating future risk assessment for decommissioned tailings dams

Carbon Trading in BRICS Countries: Challenges and Recommendations

As one of the world’s largest emerging economies, BRICS countries are playing an increasingly important role in addressing the global issue of climate change. To achieve their emissions reduction targets, these nations are actively promoting the construction of carbon trading markets. However, they face multiple challenges and obstacles in this endeavor, including issues related to market norms, financial support, technical capacity, social participation, and development needs. This research investigates the problems and challenges faced by BRICS countries in terms of building carbon trading markets through literature reviews and case studies. To address these challenges, this research strengthening international cooperation and technical support, improving market norms and provide following recommendations: conducting regulatory measures, enhancing social participation and communication, and balancing the relationship between economic development and environmental protection requirements. Furthermore, it is crucial for these nations to continue to strengthen international cooperation and collaboration, working together to promote the construction of carbon trading markets, achieving their emissions reduction targets, and ensuring long-term sustainability and economic development

Low-Confidence Samples Mining for Semi-supervised Object Detection

Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo-labels with high confidence, which ignore valuable pseudo-labels with lower confidence. Additionally, the insufficient excavation for unlabeled data results in an excessively low recall rate thus hurting the network training. In this paper, we propose a novel Low-confidence Samples Mining (LSM) method to utilize low-confidence pseudo-labels efficiently. Specifically, we develop an additional pseudo information mining (PIM) branch on account of low-resolution feature maps to extract reliable large-area instances, the IoUs of which are higher than small-area ones. Owing to the complementary predictions between PIM and the main branch, we further design self-distillation (SD) to compensate for both in a mutually-learning manner. Meanwhile, the extensibility of the above approaches enables our LSM to apply to Faster-RCNN and Deformable-DETR respectively. On the MS-COCO benchmark, our method achieves 3.54% mAP improvement over state-of-the-art methods under 5% labeling ratios

Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme

{\em Personalized PageRank (PPR)} stands as a fundamental proximity measure in graph mining. Since computing an exact SSPPR query answer is prohibitive, most existing solutions turn to approximate queries with guarantees. The state-of-the-art solutions for approximate SSPPR queries are index-based and mainly focus on static graphs, while real-world graphs are usually dynamically changing. However, existing index-update schemes can not achieve a sub-linear update time. Motivated by this, we present an efficient indexing scheme to maintain indexed random walks in expected $O(1)$ time after each graph update. To reduce the space consumption, we further propose a new sampling scheme to remove the auxiliary data structure for vertices while still supporting $O(1)$ index update cost on evolving graphs. Extensive experiments show that our update scheme achieves orders of magnitude speed-up on update performance over existing index-based dynamic schemes without sacrificing the query efficiency