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This case study introduces the STARS (Supporting Talented African American Undergraduates for Retention and Success) project, designed to foster the retention and success of academically talented African American computer science students from low-income backgrounds at Historically Black Colleges and Universities (HBCUs) in the U.S. The STARS program employs a holistic approach, integrating four primary pillars of support: academic, social, career, and financial. Specific support provided includes near-peer mentoring, technical skill development seminars, undergraduate research, and high school outreach activities. To explore the program’s effectiveness and areas of improvement, a mixed-method evaluation study was conducted, collecting data through surveys, observations, individual interviews, and focus group interviews. The findings revealed that the STARS program contributed to high levels of retention among its scholars, and the mentoring program provided valuable networking opportunities. The study suggests that the program’s comprehensive approach, tailored to scholars’ needs, and combined with a culturally affirming learning environment, facilitates the retention and success of talented African American students in computer science. 

Fine-grained urban flow inference (FUFI), which involves inferring fine-grained flow maps from their coarse-grained counterparts, is of tremendous interest in the realm of sustainable urban traffic services. To address the FUFI, existing solutions mainly concentrate on investigating spatial dependencies, introducing external factors, reducing excessive memory costs, etc., -- while rarely considering the catastrophic forgetting (CF) problem. Motivated by recent operator learning, we present an Urban Neural Operator solution with Incremental learning (UNOI), primarily seeking to learn grained-invariant solutions for FUFI in addition to addressing CF. Specifically, we devise an urban neural operator (UNO) in UNOI that learns mappings between approximation spaces by treating the different-grained flows as continuous functions, allowing a more flexible capture of spatial correlations. Furthermore, the phenomenon of CF behind time-related flows could hinder the capture of flow dynamics. Thus, UNOI mitigates CF concerns as well as privacy issues by placing UNO blocks in two incremental settings, i.e., flow-related and task-related. Experimental results on large-scale real-world datasets demonstrate the superiority of our proposed solution against the baselines. 

While stakeholder-driven approaches have been increasingly used in scenario modeling, previous studies have mostly focused on the qualitative elements, e.g., narratives and policy documents, from the stakeholders, but lack engagement of stakeholders with quantitative inputs. In this study, we conducted workshops with a stakeholder group to integrate the participatory mapping of future policies in the simulation, and to compare the environmental impacts after including the participatory mapping. A land system change model named CLUMondo was used to simulate four scenarios, i.e., Business-As-Usual (BAU), Destroying Resources in Owyhee (DRO), Ecological Conservation (EC), and Managed Recreation (MR), in Owyhee County, Idaho, United States. The InVEST models were used to assess water yield, soil erosion, and wildlife habitat under the four scenarios. The results show that the DRO scenario would decrease shrubland and increased grassland, thus leading to less water yield, more soil erosion, and deteriorated wildlife habitat anticipated through to 2050. On the contrary, the EC and MR scenarios reverse the trend and would improve these ecosystem services over the same time horizon. The stakeholder-driven policies appear to influence the spatial distribution of the land system and ecosystem services. The results help to reach a nuanced understanding of the stakeholder-driven scenarios and highlight the importance of engaging stakeholders in scenario modeling and environmental impact analysis. 

Background The increased interest in why and how trees die from fire has led to several syntheses of the potential mechanisms of fire-induced tree mortality. However, these generally neglect to consider experimental methods used to simulate fire behaviour conditions. Aims To describe, evaluate the appropriateness of and provide a historical timeline of the different approaches that have been used to simulate fire behaviour in fire-induced tree mortality studies. Methods We conducted a historical review of the different actual and fire proxy methods that have been used to further our understanding of fire-induced tree mortality. Key results Most studies that assess the mechanisms of fire-induced tree mortality in laboratory settings make use of fire proxies instead of real fires and use cut branches instead of live plants. Implications Further research should assess mechanisms of fire-induced tree mortality using live plants in paired combustion laboratory and landscape fire experiments. 

Typically, trajectories considered anomalous are the ones deviating from usual (e.g., traffic-dictated) driving patterns. However, this closed-set context fails to recognize the unknown anomalous trajectories, resulting in an insufficient self-motivated learning paradigm. In this study, we investigate the novel Anomalous Trajectory Recognition problem in an Open-world scenario (ATRO) and introduce a novel probabilistic Metric learning model, namely ATROM, to address it. Specifically, ATROM can detect the presence of unknown anomalous behavior in addition to identifying known behavior. It has a Mutual Interaction Distillation that uses contrastive metric learning to explore the interactive semantics regarding the diverse behavioral intents and a Probabilistic Trajectory Embedding that forces the trajectories with distinct behaviors to follow different Gaussian priors. More importantly, ATROM offers a probabilistic metric rule to discriminate between known and unknown behavioral patterns by taking advantage of the approximation of multiple priors. Experimental results on two large-scale trajectory datasets demonstrate the superiority of ATROM in addressing both known and unknown anomalous patterns. 

Abstract In plants, autophagy is a conserved process by which intracellular materials, including damaged proteins, aggregates, and entire organelles, are trafficked to the vacuole for degradation, thus maintaining cellular homeostasis. The past few decades have seen extensive research into the core components of the central autophagy machinery and their physiological roles in plant growth and development as well as responses to biotic and abiotic stresses. Moreover, several methods have been established for monitoring autophagic activities in plants, and these have greatly facilitated plant autophagy research. However, some of the methodologies are prone to misuse or misinterpretation, sometimes casting doubt on the reliability of the conclusions being drawn about plant autophagy. Here, we summarize the methods that are widely used for monitoring plant autophagy at the physiological, microscopic, and biochemical levels, including discussions of their advantages and limitations, to provide a guide for studying this important process. 

Abstract Developing efficient catalysts is of paramount importance to oxygen evolution, a sluggish anodic reaction that provides essential electrons and protons for various electrochemical processes, such as hydrogen generation. Here, we report that the oxygen evolution reaction (OER) can be efficiently catalyzed by cobalt tetrahedra, which are stabilized over the surface of a Swedenborgite-type YBCo 4 O 7 material. We reveal that the surface of YBaCo 4 O 7 possesses strong resilience towards structural amorphization during OER, which originates from its distinctive structural evolution toward electrochemical oxidation. The bulk of YBaCo 4 O 7 composes of corner-sharing only CoO 4 tetrahedra, which can flexibly alter their positions to accommodate the insertion of interstitial oxygen ions and mediate the stress during the electrochemical oxidation. The density functional theory calculations demonstrate that the OER is efficiently catalyzed by a binuclear active site of dual corner-shared cobalt tetrahedra, which have a coordination number switching between 3 and 4 during the reaction. We expect that the reported active structural motif of dual corner-shared cobalt tetrahedra in this study could enable further development of compounds for catalyzing the OER.