Abstract
High-throughput plant phenotyping (HTPP) enhances the throughput, resolution, and dimensionality of conventional manual phenotyping techniques. However, existing platforms face significant challenges, including high acquisition and maintenance costs, limited adaptability to field conditions, and inadequate data management capabilities. This paper introduces GREENTRIBE, an open-source, multi-sensor HTPP architecture that integrates Internet of Things sensing devices and robotics to collect, process, and manage comprehensive phenotypic and environmental data. GREENTRIBE features a multiscale sensing network, built on a sensor-independent communication protocol. An ontology-driven data management layer was designed in accordance with common standards and metadata guidelines, ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) (meta)data. The architecture combines Computer Vision and Artificial Intelligence data analysis pipelines with a process-based crop model for data assimilation, allowing the quantitative traits derived from the sensing layer to be linked to contextual data (genotype, environment, and management conditions). The architecture and performance indicators are presented, demonstrating efficient data collection, processing, and management. Phenotyping is the cornerstone of GREENTRIBE, offering a valuable platform for generating data-rich, reproducible workflows, multimodal datasets, and analysis systems with high impact on Precision Agriculture, improving real-time monitoring, input application, and environmental impacts assessment towards maximized crop productivity, quality, and sustainability.

Abstract
In the competitive landscape of higher education, institutions increasingly rely on international rankings to secure funding, attract talent, and enhance their global reputation. Concurrently, these institutions have expanded their presence on social media, utilizing sophisticated posting strategies not only to disseminate information but also to boost recognition and engagement. This study examines the relationship between the rankings of Higher Education Institutions (HEIs) and their social media posting strategies. We collected and analyzed tweets from 22 HEIs featured in a consolidated ranking system, focusing on various features of their social media posts. The analysis identified six distinct clusters of posting strategies. This paper categorizes the HEIs into these clusters and discusses the implications of differing social media strategies on their rankings The findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.

Abstract
ABSTRACT The study aims to understand how, to what extent, and through what mechanisms startups contribute to the achievement of the sustainable development goals (SDGs), distinguishing between impact-driven initiatives and approaches in which sustainability is used instrumentally. To this end, a mixed-methods methodology was applied, combining quantitative and qualitative analysis on a sample of 1864 startups accelerated by Y Combinator. The results indicate that the most addressed SDGs are SDG 13 (Climate Action) and SDG 3 (Good Health and Well-being), with an emphasis on clean technologies, low-carbon models, data-based climate monitoring, personalized and preventive medicine, mental health, and clinical data management. In contrast, goals related to biodiversity and reducing inequalities are less represented, revealing gaps and areas with potential for expansion. The study's contributions include identifying patterns and gaps in startups' engagement with the SDGs, reinforcing the importance of public policies and incentives to balance efforts in less addressed areas, and demonstrating the central role of digital technologies and data analysis in promoting sustainability.

Abstract
Data leakage is a critical issue in deep learning inflating performance and compromise validity, especially in sensitive areas like medical imaging. This study systematically evaluates two common leakage types in oral squamous cell carcinoma classification from histopathology images: (1) preprocessing leakage (global normalization before dataset splitting) and (2) a severe sample-related (patient-related) contamination scenario created by mixing closely related original and augmented images across splits. We trained 11 CNN and Transformer-based models on a public oral cancer histopathology dataset, benchmarking results against published leakage-free baselines. The results obtained show that the configuration with random splitting of original and augmented images (Scenario 2) artificially increased accuracy by up to 18% (mean +14.3%) compared to leakage-free conditions, while the preprocessing-based leakage (Scenario 1) showed smaller deviations (+1.8%). These inflated metrics arise from a combination of cross-split contamination between closely related samples and increased dataset redundancy, rather than genuine gains in generalization ability. Transformers improved leak-free accuracy (+3.9%) but degraded performance in Scenario 2 (-1.4%), revealing sensitivity to sample-specific biases. The observed performance gains under data leakage conditions are methodological artifacts that undermine clinical reliability, with a severe sample-related contamination scenario (Scenario 2) with random splitting of original and augmented images being particularly detrimental due to its promotion of non-generalizable feature learning. The quantitative benchmarks established here-including a mean accuracy gap of 12.5% (Scenario 2 vs. Scenario 1) across 11 models and Transformer architectures’ sensitivity to contamination-reveal fundamental tradeoffs between metric inflation and model trustworthiness. These findings establish quantitative benchmarks for leakage impacts in medical imaging and inform future guidelines for trustworthy AI development in pathology. © The Author(s) 2026.

Abstract
Wireless Sensor Networks (WSNs) are increasingly evolving toward intelligent distributed systems in which local sensing, on-device inference, and collaborative model training are becoming central to scalable Internet of Things (IoT) deployments. However, the practical adoption of Federated Learning (FL) in WSN-oriented environments remains constrained by three major challenges: limited and unevenly depleted node energy, heterogeneous non-IID local data distributions, and variable client reliability during collaborative training. This paper proposes a Trust- and Energy-Aware Federated Learning (TEA-FL) framework specifically designed for resource-constrained WSN settings, in which client participation and server-side aggregation are jointly guided by residual energy estimates and dynamically updated trust scores. The proposed method prioritizes reliable, energy-efficient sensor nodes while reducing the impact of weakly aligned or low-quality local updates during global aggregation. The framework is evaluated on two representative WSN/IoT-oriented proxy benchmarks, Human Activity Recognition (HAR) and UNSW-NB15 intrusion detection, under both IID and Dirichlet-based non-IID federated partitions. Under non-IID HAR partitioning, TEA-FL improved final accuracy from 0.6752 with FedAvg to 0.7636 and final Macro-F1 from 0.5623 to 0.7185. On the more challenging non-IID UNSW-NB15 benchmark, TEA-FL achieved the highest final Macro-F1, 0.3711, compared with 0.3230 for FedAvg and 0.3323 for the trust-only baseline, although with a lower final accuracy. These results indicate that TEA-FL is particularly useful when final-round robustness, class-balanced behavior, and client sustainability are more relevant than maximizing a single peak intermediate accuracy value. Additional ablation and unreliable-client experiments further show that the trust-energy-aware aggregation component is particularly influential and that TEA-FL can improve behavior under selected low-quality participation scenarios, although it should not be interpreted as a complete Byzantine-robust defense. Overall, the findings suggest that jointly modeling update consistency and residual energy offers a practical, lightweight pathway toward more dependable and sustainable federated intelligence in next-generation WSN and IoT deployments.

Abstract
The polarization curve characteristics of proton exchange membrane (PEM) hydrogen electrolyzers lead to large variations in the equivalent load impedance over the operating current range. This results in a varying closed-loop system time response when traditional fixed-gain PI controllers are employed. In this work, the design and experimental validation of a 3-phase interleaved buck converter controlled via a proposed adaptive lead-lag current control strategy for a PEM hydrogen electrolyzer load is presented. The incremental load conductance method is used to obtain a control-oriented model of the converter-electrolyzer system, enabling real-time calculation of controller parameters via pole-zero cancellation and user-specified transient performance. A laboratory prototype is implemented to experimentally verify the approach under step-load changes, ramp-load changes, and 50% input voltage sag conditions. The results show less than 1% current ripple, identical transient performance over the entire operating range, and improved disturbance ride-through performance compared to a traditional PI controller. The proposed approach offers a viable and robust control solution for high-current PEM electrolyzer applications.