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关键词： Integrated curriculum   Computer Science   Justice-oriented curriculum   Co-design   Teacher collaboration   Integrated experiential learning   Adaptive expertise   Research practice partnership  

摘要： The purpose of this cross-case analysis is to understand how mathematics and English Language Arts (ELA) educators integrated social justice (SJ)-oriented computer science (CS) instruction into middle school classrooms. An integrated curriculum synthesises content and methods from two or more fields to teach curricular concepts and solve complex problems. This research explores data collected as part of a research-practice partnership (RPP) to deepen the field's understanding of achieving this integration. Through co-designing and implementing instructional units that integrate social justice-oriented CS instruction, we uncovered important themes and patterns that make CS accessible to teachers across varying contexts. These themes encompass the demonstration of the teachers' (1) willingness to collaborate, (2) design of integrated experiential learning, and (3) adaptive expertise throughout the process. The study aims to highlight actionable conditions and factors for practitioners, teacher educators, and curriculum developers interested in adapting this work to create a justice-oriented integrated curriculum in their context. We meet this purpose by answering the research question: How do middle school teachers describe the co-learning and collaborative design process of integrating justice-oriented computer science instruction into mathematics and ELA classrooms?

关键词： Synchronization   Trajectory   Sufficient conditions   State feedback   Computer science   Vectors   Training   System performance   Symbols   Reinforcement learning   Drive-response Boolean networks (BNs)   local synchronization   pinning control  

摘要： A pinning control strategy for the local synchronization of drive-response Boolean networks is proposed in this article. A necessary and sufficient condition of the local synchronization is given, along with the maximum initial state set guaranteeing the synchronization. A procedure for determining pinning nodes with the minimum number and designing a state feedback for each pinning node, leading to a local synchronous drive-response Boolean network, is provided. Finally, a gene regulation network for the D. melanogaster segmentation polarity is elaborated to illustrate the effectiveness of the proposed results.

关键词： Forest fragmentation   Deforestation   Satellite data   Landscape metrics   Landscape fragmentation tool  

摘要： This study investigated the spatial and temporal changes of forest area and fragmentation in Quang Nam province, Vietnam, from 1988 to 2022, using remote sensing and Geographic Information System (GIS) techniques. By analyzing multi-temporal Landsat imagery and applying the Landscape Fragmentation Tool (LFT), we assessed changes in forest cover and the extent of forest fragmentation. The results showed significant changes in forest area, which initially increased due to reforestation programs, then decreased from 2010 to 2022, mainly due to urban expansion, encroachment on agricultural land, and infrastructure development. Landscape metrics, including patch density, edge density, and largest patch index, showed increasing fragmentation, with the area of large core forest shrinking from 80.65% in 1988 to 69.69% in 2022. Fragmented forest classes, such as patch, edge, and perforated forests, increased significantly, indicating rising pressures on forest ecosystems. These changes have negatively affected biodiversity by reducing habitat connectivity and increasing edge impacts. The findings highlight the urgent need for sustainable land use planning, reforestation efforts, and conservation strategies to mitigate further fragmentation and preserve biodiversity. This study has contributed valuable insights into the dynamics of forest change and fragmentation in Vietnam, providing a foundation for future conservation efforts and policy development to maintain ecological integrity in the region.

关键词： Curricula  

摘要： Mathematics courses make up a good percentage of the undergraduate curriculum in the Computer Science and Engineering discipline. It is however important to ascertain how the mathematics courses impact the programming skills of the students. This article examines the impact of mathematics ancillary courses on the Computational Programming Intelligence (CPI) of Computer Science and Engineering students. Using the results of Computer Science students on mathematics and programming courses for seven (7) sessions, Random forest regression and K-means clustering machine learning models were used to study the relationship between the ancillary courses and their performance in programming courses. A Pearson correlation coefficient was computed to assess the linear relationship between five (5) Mathematics ancillary courses and the programming courses. A significant positive correlation (0.29, 0.27, 0.20, 0.10, and 0.09, p = 0.02) was obtained with Linear algebra having the highest and Mathematical methods the least. Consequently, variable importance results show that linear algebra had the highest impact on CPI while Mathematical methods had the least in the following order (29.11%, 20.39%, 19.80%, 17.15%, and 13.55%). Mostly female students of age range 19–20 were found to have been positively impacted more by the mathematics courses. A curriculum guide was presented based on the findings. © 2025 Wiley Periodicals LLC.

关键词： Computer science   Human behaviour   Policy  

摘要： Virtual chatbots that simulate conversations with famous actors or sci-fi characters can have real-world consequences.

关键词： Testing   Training   Reinforcement learning   Robot kinematics   Manipulators   Informatics   Computer science   Vectors   Sun   Semantics   Disentangled representation learning   out-of-distribution (OOD) generalization   reinforcement learning (RL)  

摘要： Out-of-distribution (OOD) generalization is critical for applying reinforcement learning algorithms to real-world applications. To address the OOD problem, recent works focus on learning an OOD adaptation policy by capturing the causal factors affecting the environmental dynamics. However, these works recover the causal factors with only an entangled or binary form, resulting in a limited generalization of the policy that requires extra data from the testing environments. To break this limitation, we propose generalizable causal reinforcement learning (GCRL) to learn a disentangled representation of causal factors, on the basis of which we learn a policy that achieves the OOD generalization without extra training. For capturing the causal factors, GCRL deploys a weakly supervised signal with a two-stage constraint to ensure that all factors can be disentangled. Then, to achieve the OOD generalization through causal factors, we establish the dependence of actions on the learned representation and optimize the policy model across multiple environments. Experimental results show that the established dependence recovers the correct relationship between causal factors and actions when the learned policy could address the target tasks in training environments. Benefiting from the recovered relationship, GCRL achieves the OOD generalization on eight benchmarks from Causal World and Mujoco. Moreover, the policy learned by our model is more explainable and can be controlled to generate semantic actions by intervening in the representation of causal factors.

关键词： Convergence   Perturbation methods   Approximation algorithms   Gradient methods   Vectors   Noise measurement   Accuracy   Taylor series   Computer science   Estimation   Balanced generalized simultaneous perturbation stochastic approximation (B-GSPSA)   generalized random directions stochastic approximation (GRDSA)   generalized simultaneous perturbation-based gradient search (GSPGS)   generalized smoothed functional (GSF) procedure   GSPSA   stochastic optimization  

摘要： We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation estimators and later present the balanced versions of these. We extend this idea further and present the generalized smoothed functional and generalized random directions stochastic approximation estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and nonasymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings.

关键词： Engineering education  

摘要： Background: Despite the vital function of engineering and computer science (Eng & CS) to innovation and economic development, retention within Eng & CS programs remains a major challenge in the U.S. educational system. Despite extensive research on influential factors of retention, there is a gap in our understanding of how these factors rank in relative importance and their temporal variations. Purpose: This study aims to identify and compare the factors and demographic variables that predict retention in Eng & CS programs at two critical timepoints: from the first to the third year, and from the third year to completion within 6 years. Method: The study applied random forest analyses to a longitudinal dataset from the nationally representative BPS 12/17 survey. Results: High school academic preparation is the most important predictor of both 3-year and 6-year retention. Psychological factors are more crucial to retention in the early stages, while a more diverse, practical, career-oriented set of factors gains importance when it comes to the 6-year retention period. Conclusions: The study advocates for stage-wise interventions, such as enhanced academic preparation programs and robust support networks in the early stages, and career services and practical support systems as students advance. The findings also highlight the importance of peer support networks and tailored support from institutions. © 2025 American Society for Engineering Education.