This preregistered replication study examined how physical proximity in the classroom is associated with peer perceptions of social status, specifically likeability and popularity. We aimed to replicate and extend the study by van den Berg and Cillessen (2015) using data from 719 students across 33 elementary classrooms (grades 4-6; Mage = 10.27; 51.6% boys). We applied the Social Relations Model and mixed-effects modeling to examine (1) whether proximity between classmates is concurrently associated with likeability and popularity, (2) whether longitudinal changes in proximity lead to changes in likeability and popularity, (3) whether these findings differ for friends versus non-friends, and (4) whether they depend on how proximity is operationalized. We replicated previous findings by showing that classmates seated closer together concurrently rated each other as more popular and likeable. Furthermore, when given the opportunity to create their own seating arrangements, students placed classmates they found likeable and popular closer to themselves. We extended previous findings by showing that increased proximity between two classmates over time led to an increase in mutual liking, but not perceived popularity. Further, findings did not differ significantly between friends and non-friends. Finally, because extant studies differ in how proximity is measured, we tested an operationalization of proximity in terms of desk clusters. This alternative operationalization produced similar results to its continuous counterpart, highlighting the conceptual and practical value of the current operationalization for future research.
Land governance persistently fails to achieve its stated objectives, producing unintended outcomes, coordination failures, and inequality. This paper argues that a major reason is the treatment of land use, land rights, and land value as parallel administrative domains rather than as mutually constitutive components of one recursive governance structure. The Land Triadic Nexus, developed here, explains governance outcomes as a function of the degree of alignment among these three domains. The framework is constructed through a targeted review of existing land governance frameworks, from which four theoretical foundations are developed and four propositions derived. Three foundations are analytical, drawing on institutional economics and property rights theory, game theory, and adaptive governance theory. The fourth is normative, grounded in political philosophy and critical land studies, and establishes land as a shared trust defining what alignment is for. Together these traditions yield three claims. Configurations of land use, rights, and value are strategically produced and defended by actors. Alignment among the three domains is the condition under which planning interventions achieve their intended outcomes. And land governance is an ongoing institutional search for better alignment, constrained by path dependence, capture, and culturally institutionalised models of governance. The propositions are conjectures rather than validated findings, and define the empirical agenda of the research programme the paper opens. Two illustrations, Sayung in Indonesia and Haven-Stad in Amsterdam, show how the framework identifies mechanisms of misalignment that existing frameworks struggle to explain. The paper closes with a research agenda and positions the framework as extending, not replacing, existing scholarship.
Microbial communities can efficiently mediate aerobic ammonia oxidation even at acidic pH. However, little is known about the strategies that enable them to simultaneously mitigate acidic and nitrosative stresses. Here, we integrate genome-resolved meta-omic analyses with chemical measurements to infer the composition, metabolic exchange, and stress adaptations of microbial consortia in three acidic nitrification bioreactors operated between pH 2.0 and 5.0. At pH 5.0, the dominant ammonia-oxidising bacterium (AOB) across all conditions was a novel Nitrosococcaceae species, designated “Candidatus Nitrosoglobus kelleri.” It reached a relative abundance of up to 56% and possessed genes for ammonia oxidation, aerobic respiration, and carbon fixation. At pH 2.0, the community was strongly simplified and dominated by "Ca. Nitrosoglobus kelleri" together with Mycobacterium species; genome-resolved and transcriptional evidence suggested potential metabolic linkages related to carbon and nitrogen compound cycling. Both “Ca. Nitrosoglobus kelleri” and Mycobacterium spp. concurrently activate metabolic mechanisms to maintain intracellular pH homeostasis, detoxify reactive nitrogen species, and reinforce their cellular envelopes. Despite inhabiting aerobic bioreactors, consortia members also expressed denitrification genes at high levels, likely to eliminate reactive nitrogen species such as nitrite and nitric oxide produced during incomplete nitrification, with a trade-off in respiratory efficiency. Collectively, these findings provide critical insights into the metabolic adaptations of minimalistic microbial communities at extremely low pH. In addition to enhancing understanding of microbial nitrogen cycling, this work has potential implications for improving wastewater treatment technologies through acidic nitrification processes.
Automated ECG classification is increasingly important for wearable and Internet of Medical Things (IoMT) applications, where deep learning models must provide accurate predictions under strict computational and memory constraints. Knowledge distillation can improve model efficiency, but conventional approaches typically rely on separately pre-trained teacher networks. To address these limitations, this study proposes End-to-End Internal Augmentation and Curriculum-based Knowledge Distillation (EIA-CKD), a lightweight end-to-end framework that generates and distills supervisory knowledge within a single network. During training, multiple views are generated internally from each ECG image and processed through a shared backbone along a frozen-consensus path, where gradient tracking is disabled. The resulting logits are aggregated by an attention-based consensus module to form a stable consensus representation that serves as an internal source of supervisory knowledge. A curriculum-based training strategy progressively increases the contribution of the internal consensus, allowing the model to gradually transition from ground-truth supervision to internally generated knowledge during training. The proposed framework relies on a lightweight CNN backbone that combines depthwise separable convolutions(DSC) with convolutional block attention modules (CBAM) to reduce computational overhead while preserving representational power. Experimental evaluation on the PTB-XL and Chapman ECG datasets confirms the effectiveness of the proposed EIA-CKD approach. With only 102 K trainable parameters, the proposed framework achieves AUROC scores of 95.60% and 99.16% and classification accuracies of 84.67% and 94.23% on PTB-XL and Chapman, respectively. These results show that EIA-CKD provides competitive classification performance with a substantially lower computational footprint, supporting its potential for real-time ECG analysis in wearable and resource-constrained IoMT environments.
Background AI-driven Adaptive learning technologies (ALTs) are widely used in primary education. These technologies have shown promising short-term results in arithmetic development. However, their long-term impact remains unclear. Aims This longitudinal observational study with a staggered introduction of the ALT investigated how the arithmetic ability of primary school students developed from grade 1 to grade 4 and how this development differed between students who used an ALT for arithmetic practice and those who did not. We also explored how individual and school characteristics moderated any developmental differences. Sample Data were collected over three years and included 7885 children, of whom 4603 (58.4%) used an ALT and 3282 (41.6%) did not. Method Multigroup SEM growth curve analyses compared arithmetic development across the ALT and control groups. Hierarchical models (linear and nonlinear) were tested, including time-invariant covariates (gender, socioeconomic status, prior arithmetic ability, and school size). Results Students who used the ALT showed a trend towards greater growth than those who did not, with small-to-medium effect sizes. Gender and individual SES differences were small in both conditions. Schools with a higher proportion of lower-SES students showed greater growth when using the ALT. The ALT also appeared to mitigate negative associations between school size and arithmetic development. Conclusions Use of the ALT was associated with numerically greater, though not statistically significant, arithmetic development in the long-term. Larger schools and schools with a higher percentage of lower SES students may consider using an ALT to support teachers in delivering tailored instruction.
