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In recent years, there has been a rise in recognition of the need for computing education to bridge the gap between academia and industry. In addition, educational researchers are also interested in increasing student engagement by grounding learning experiences in real-life concerns, community issues, or personal interests. Unfortunately, traditional lecture-based teaching techniques often fail to prepare students for the challenges they will face in real-world software development scenarios. Project-Based Learning (PjBL) takes a different approach by immersing students in real-world software engineering projects, allowing them to apply theoretical knowledge in practical contexts, building practical skills, fostering critical thinking, and improving problem-solving abilities. Prior literature reviews have explored aspects of PjBL in computing education, such as communication support, educational effectiveness, sprint organization, and capstone course design. However, no literature review extensively and comprehensively examines the following questions as a whole: where PjBL is used, how it is taught, why it should be used, and what challenges to expect in software-related computing courses. The review takes a systematic approach, incorporating a thorough search strategy across four academic databases and targeting keywords associated with PjBL and software computing in higher education. A total of 34 PjBL course attributes were extracted from 184 selected primary studies, which contributed to answering six research questions: (1) What computing courses use PjBL? (2) What is the nature of software projects used? (3) How are these projects organized? (4) How are students assessed and evaluated? (5) What are the reported impacts of PjBL? and (6) How are students supported throughout the projects? The literature review makes four key contributions: a description of the nature of software projects used and how these projects are organized, a highlight of the impacts of PjBL and the methods used to measure those impacts, a summary of the various forms of support provided to students throughout their projects, and the list of challenges encountered in implementing PjBL and recommendations to alleviate those challenges. This comprehensive review offers new insights and serves as a catalog of best practices for computing educators. 

Abstract Reliable estimates of climate sensitivity require understanding how patterns of surface temperature change influence the global radiative feedback. Here we present a theoretical basis for this pattern effect as it relates to the longwave clear sky feedback. A moist adiabatic feedback framework is developed that partitions the feedback into components associated with locally determined moist adiabatic processes and components associated with deviations therefrom, such as due to nonlocal influences and relative humidity changes. Applying this feedback framework to simulations forced by transient and equilibrium patterns of sea surface temperature change reveals that the pattern effect is driven by different physical processes in different geographic regions. In the subtropics, the more stabilizing feedback under transient climate change is explained by a more negative relative humidity feedback. Over the Southern Ocean, the less stabilizing feedback under transient climate change occurs due to the muted surface warming there, which promotes a weak surface temperature feedback; furthermore, for an idealized pattern of change in which the transient sea surface temperature change is uniformly increased but retains the same structure, the pattern effect essentially disappears. The moist adiabatic feedback framework demonstrates that the evolving zonal-mean longwave clear sky feedback—towards stabilization at high latitudes and destabilization at low latitudes, as the climate approaches equilibrium—is controlled by processes, specifically surface temperature and relative humidity feedbacks, not isolated by conventional feedback analysis. In the global mean, the destabilization effect proves larger, receiving additional contributions from small but geographically extensive differences in the fixed-relative humidity atmospheric temperature feedback. 

Field-theoretic simulations that rely on a partial saddle-point approximation have become powerful tools for studying complex polymer materials. The computational cost of such simulations depends critically upon the efficiency of the iterative algorithm used to identify a partial saddle-point field configuration during each step of a stochastic simulation. We introduce a new algorithm for this purpose that relies on a physically motivated approximation in which the linear response of the density to a small change in a pressure-like field is approximated by the response of a hypothetical homogeneous system. The computational cost of the resulting algorithm is significantly less than that of the commonly used Anderson mixing algorithm. 

Mn²⁺doping of CsPbBr₃perovskite magic‐sized clusters (PMSCs) has been reported previously, where PMSCs with first excitonic absorption and photoluminescence (PL) around 425 nm were reported originally, followed by Mn²⁺‐doped PMSCs with host absorption and PL around 400 nm. There, the observed 25 nm blueshift was attributed to smaller PMSCs or the Cl⁻ions introduced by MnCl₂as dopant precursor. However, subsequent studies suggest that the 400 nm band may instead be due to ligand‐assisted metal halide molecular clusters (MHMCs), which lack the A component of perovskite. This raises the question whether the originally claimed Mn²⁺‐doped PMSCs are actually MHMCs. To unambiguously address this issue, Mn²⁺‐doped CH₃NH₃PbBr₃PMSCs were synthesized with PL at both 440 nm, attributed to the PMSC, and at 600 nm, attributed to Mn²⁺. Blueshifting of the host absorption and PL bands due to Cl⁻codoping is avoided by selecting MnBr₂as dopant precursor rather than MnCl₂. Dopant incorporation into PMSCs is further supported by PL excitation, time‐resolved PL, and electron paramagnetic resonance studies. This work provides direct and strong evidence of successful Mn²⁺doping in PMSCs. 

SMAX1-LIKE (SMXL) proteins in plants are cellular signaling hubs, many of which are posttranslationally regulated by karrikins from smoke, the plant hormones strigolactones (SLs), and/or cues such as light and nutrients. SMXL proteins control diverse aspects of growth, development, and environmental adaptation in plants through transcriptional corepression and interactions with transcriptional regulator proteins. In flowering plants, the SMXL family comprises four phylogenetic clades with different roles. Functions of the aSMAX1 clade include control of germination and seedling development, while the SMXL78 clade controls shoot architecture. We investigated how SMXL roles are specified inArabidopsis thaliana.Through promoter-swapping experiments, we found thatSMXL7can partially replicateSMAX1function, butSMAX1cannot replaceSMXL7. This implies that the distinct roles of these genes are primarily due to differences in protein sequences rather than expression patterns. To determine which part of SMXL proteins specifies downstream control, we tested a series of protein chimeras and domain deletions of SMAX1 and SMXL7. We found an N-terminal region that is necessary and sufficient to specify control of germination, seedling growth, or axillary branching. We screened 158 transcription factors (TFs) for interactions with SMAX1 and SMXL7 in yeast two-hybrid assays. The N-terminal domain was necessary and/or sufficient for most of the 33 potential protein–protein interactions that were identified for SMAX1. This finding unlocks different ways to engineer plant growth control through cross-wiring SMXL regulatory “input” and developmental “output” domains from different clades and lays a foundation for understanding how functional differences evolved in the SMXL family.