Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations.

For sustainable agriculture practices to be achieved as a result of changing climates and growing hazards to the environment, improving resilience in plants is crucial. Stress-Associated Proteins (SAPs) have an important role in helping plants react to abiotic stress conditions such as drought, salinity, and changes in temperature. This study underlines the ability of the SAP gene family to promote stress adaptation mechanisms by presenting a thorough analysis of the gene family across 86 distinct plant species and genera. We present an optimized Hybrid Algorithm for Robust Plant Stress (HARPS), a unique machine learning (ML)-based system designed to efficiently identify and classify plant stress responses. A comparison with conventional ML models shows that HARPS substantially reduces computational time while achieving higher accuracy. This efficiency makes HARPS ideal for real-time agricultural applications, where precise and quick stress detection is essential. With the help of an ablation study and conventional evaluation metrics, we further validated the effectiveness of the model. Overall, by strengthening crop monitoring, increasing resilience, lowering dependency on chemical inputs, and enabling data-driven decision-making, this research advances the objectives of sustainable agriculture production and crop protection. HARPS facilitates scalable, resource-efficient stress detection essential for adjusting to climatic uncertainty and mitigating environmental consequences.

Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions.

Background: Embodied artificial intelligence (EAI), integrating advanced AI algorithms with robotic platforms capable of sensing, planning, and acting, has emerged as a transformative approach in healthcare delivery. This systematic review synthesizes evidence on robotic perception, decision-making, and clinical impact of EAI systems in healthcare settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for studies published between January 2020 and August 2025. Seventeen studies met eligibility criteria, spanning four domains: surgical assistance, rehabilitation, hospital logistics, and telepresence. The protocol was prospectively registered in PROSPERO under ID: CRD420261285936. Results: Perception architectures predominantly employed multimodal sensor fusion, combining vision with force/torque, depth, and physiological signals. Decision-making approaches included imitation learning, reinforcement learning, and hybrid symbolic-neural control. Key findings indicate that surgical robots demonstrated consistency advantages in specific experimental tasks, rehabilitation robotics produced statistically significant improvements (SMD = 0.29) across 396 randomized controlled trials, and both logistics and telepresence systems achieved very high operational success levels. Nonetheless, important barriers remain, including limited external validation, small sample sizes, and insufficient cost-effectiveness data. Conclusions: Future research should prioritize standardized benchmarks, prospective multicenter trials, and patient-centered outcome measures to facilitate clinical translation of EAI technologies.