While ferromagnetism and antiferromagnetism are well-established classes of magnetic order, a third class of collinear magnetic order, termed altermagnetism, has recently attracted scientific interest. We measured magnetic circular dichroism (MCD) in core-level photoemission (XPS) at the Ru 2p3/2 and 2p1/2 core levels in epitaxial RuO2(110)/TiO2(110) films using circularly polarized x rays at 6 keV, as well as x-ray magnetic circular dichroism (XMCD) in resonant x-ray absorption at the Ru M3,2 (3p3/2 and 3p1/2) edges. Charge transfer multiplet calculations show that the MCD-XPS and the XMCD can be explained by an altermagnetic locking of Ru magnetic moments and a distorted crystal field orientation. The distortion is caused by the epitaxial strain. The collinear magnetic moments in RuO2 occupy sublattice sites with distorted octahedral crystal fields that are rotated by 90 degrees with respect to each other. A change in the sign of the MCD-XPS at different sample positions indicates the presence of altermagnetic domains with the size of around hundreds of micrometers.
Positronium lifetime imaging (PLI), an emerging extension of conventional positron emission tomography (PET) imaging, offers a novel window for probing the submolecular properties of biological tissues by imaging the mean lifetime of the positronium atom. Currently, the method is under rapid development in terms of reconstruction and detection systems. Recently, the first in vivo positronium lifetime imaging (PLI) of the human brain was performed using the J-PET scanner utilizing the 68Ga isotope. However, this isotope has limitations due to its comparatively low prompt gamma yields, which is crucial for positronium lifetime measurement. Among alternative radionuclides, Sc-44 stands out as a promising isotope for PLI, characterized by a clinically suitable half-life (4.04 h) emitting 1157 keV prompt gamma in 100% cases after the emission of the positron. This study reports the first experimental demonstration of PLI with Sc-44 , carried out on a NEMA-image quality (IQ) phantom using the Modular J-PET tomograph-the first plastic scintillators-based PET scanner.
This research proposes the design of an autonomous fire detection system based on a Raspberry Pi 3 B+, combined with smoke and flame sensors, enabling real-time monitoring and increased reactivity to fire hazards. To improve detection accuracy and risk prediction, Machine Learning (ML) algorithms, including Random Forest and Linear Regression, were applied to classify hazard levels and anticipate critical situations. The implementation of these models has enabled accurate classification of risk levels, guaranteeing real-time alerts and rapid decision-making. In addition, anomaly detection techniques based on the Random Forest model have been integrated to identify unusual sensor behavior, ensuring the reliability of the data collected and the correction of any measurement errors. A major contribution of this research lies in the fact that the systems were developed to be adaptable in order to serve areas (i.e., rural and agricultural areas without sufficient Internet access and/or cloud infrastructure) that had little or no access to the Internet. By integrating an embedded, independent, and efficient solution, this system offers a viable alternative to conventional monitoring methods. As part of this research, model performance was evaluated using a confusion matrix for classification accuracy, with the identification of abnormal sensor behavior (anomalies). The performance of each model was evaluated by implementing five-fold stratified cross-validation to confirm their accuracy. The logistic regression model yielded an overall average accuracy of 0.9446 ± 0.0600, with an overall F1 score of 0.9173 ± 0.0744 and a total recall of 0.9250 ± 0.0608. On the other hand, the random forest model produced an overall average accuracy of 0.9860 ± 0.0172, with an overall F1 score of 0.9740 ± 0.0319 and a total recall of 0.9733 ± 0.0327. The random forest demonstrated reliable and balanced classification of fire risk levels compared to the other models in this study. The safety and sustainability of agricultural crops are directly supported by the results of this research, with a reduction in agricultural fire risk through the protection of natural resources and increased resilience of farms through better preparedness for natural disasters. The transition of agriculture to smart systems involves integrated smart approaches to agricultural risk management in order to achieve safer, more sustainable, and more resilient agriculture.
Sustainable water management in agriculture is a major challenge, particularly in regions facing water scarcity and the growing impacts of climate change. The lack of efficiency of traditional irrigation methods often leads to water waste, reduced productivity, and increased pressure on natural resources. In this context, it is imperative to develop innovative solutions to optimize water use while maintaining agricultural performance. This paper proposes a smart irrigation system based on the internet of things (IoT) and cloud computing. The system incorporates several sensors to measure key environmental parameters, such as temperature, air humidity, soil moisture, and water level. An embedded ESP32 microcontroller collects and transmits the data to the thingsBoard cloud platform, where it is analyzed in real time to determine precise irrigation needs. The system’s algorithm automatically makes the necessary decisions to activate or deactivate the irrigation pump, ensuring optimal and accurate water management. Experimental results demonstrate that the system significantly reduces water waste while optimizing irrigation based on the actual needs of the soil and crops. Real-time measurements and automated decision-making ensure accurate and efficient irrigation that adapts to fluctuations in environmental conditions. Performance analysis shows that the proposed approach significantly improves water resource management compared to traditional methods. The integration of cloud computing and the IoT facilitates remote monitoring and automated decision-making, making the system adaptable to a variety of crops and agricultural lands. The estimated cost of implementing the smart irrigation system is approximately $44.00, confirming its economic feasibility and appeal to small and medium-sized farms seeking to optimize water use. This solution also helps to build farmers’ resilience to climate change and water scarcity. The system presented represents a significant advance in the field of smart and sustainable irrigation. By optimizing water use and improving agricultural productivity, the system directly contributes to food security, water resource conservation, and climate resilience. Thus, this study provides a replicable and adaptable model for the development of large-scale smart and sustainable agricultural solutions.
Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames, offering low latency, high dynamic range, and motion robustness. DVS-PedX (Dynamic Vision Sensor Pedestrian eXploration) is a neuromorphic dataset designed for pedestrian detection and crossing-intention analysis in normal and adverse weather conditions across two complementary sources: (1) synthetic event streams generated in the CARLA simulator for controlled "approach-cross" scenes under varied weather and lighting, comprising 198 sequences (117 good weather, 81 bad weather) with 178,200 total frames; and (2) real-world JAAD dash-cam videos (346 clips) converted to event streams using the v2e tool, preserving natural behaviors and backgrounds. Each sequence includes paired RGB frames, per-frame DVS "event frames" (33 ms accumulations), and frame-level binary labels (crossing vs. not crossing). We provide raw AEDAT 2.0/AEDAT 4.0 event files, AVI DVS video files, and metadata for flexible re-processing. Baseline experiments using spiking neural networks (SNNs) with SpikingJelly achieve an F1-score of 86.37% on the synthetic validation set, revealing a sim-to-real gap that motivates domain adaptation and multimodal fusion. DVS-PedX aims to accelerate research in event-based pedestrian safety, intention prediction, and neuromorphic perception.
