Purpose: To elucidate the effects during the vegetative growth of pre-sowing magnetic treatments on water relations, photosynthesis and plant growth in tomato (Vyta) plants under greenhouse conditions. Materials and methods: Tomato seeds were exposed to full-wave rectified sinusoidal non-uniform magnetic fields (MFs) induced by an electromagnet at 120 mT (rms) for 10 min and at 80 mT (rms) for 5 min. Non-treated seeds were used as controls. Plants were grown in polystyrene trays and water relations, photosynthesis and plant growth were measured. Results: Plants from magnetically-treated seeds maintained better leaf water status in terms of increases in leaf water potential, leaf osmotic potential, leaf turgor potential and relative water content, and decreases in stomatal conductance and transpiration rate. Net photosynthesis rate, chlorophyll a, chlorophyll b, carotenoids and total chlorophyll contents increase in plants from magnetically-exposed seeds compared to controls. The MF treatments leads to a notable increase in root length, plant height, root and shoot dry mass, leaf area per plant, and root and shoot relative growth rates. Conclusions: Application of full-wave rectified sinusoidal non-uniform MF as a pre-sowing treatment has the potential to improve tomato plant vegetative growth through the enhancement of water relations and photosynthesis.

This article explores the patents of solar energy technologies in the organic Rankine cycle (ORC) applications. The conversion of low-quality thermal energy into electricity is one of the main characteristics of an ORC, making efficient and viable technologies available today. However, only a few and outdated articles that analyze patents that use solar energy technologies in ORC applications exist. This leads to a lack of updated information regarding the number of published patents, International Patent Classification (IPC) codes associated with them, technology life cycle status, and the most relevant patented developments. Thus, this article conducts a current investigation of patents published between January 2010 and May 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and keywords. One thousand two hundred ninety-nine patents were obtained as part of the study and classified in F and Y groups of the IPC. The time-lapse analyzed was between January 2010 and May 2022. In 2014 and 2015, a peak of published patents was observed. China (CN) was the country that published the most significant number of patents worldwide. However, the European Patent Office (EP), the World Intellectual Property Organization (WO), and the United States (US) publish the patents with the highest number of patent citations. Furthermore, the possible trend regarding the development of patents for each technology is presented. A high-performance theoretical ORC plant based on the patent information analyzed by this article is introduced. Finally, exploration of IPC revealed 17 codes related to solar energy technologies in ORC applications not indexed in the main search.

This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG.

Developing reliable railway fault detection systems is crucial for ensuring both safety and operational efficiency. Various artificial intelligence frameworks, especially deep learning models, have shown significant potential in enhancing fault detection within railway infrastructure. This study explores the application of deep learning models for railway fault detection, focusing on both transfer learning architectures and a novel classification framework. Transfer learning was utilized with architectures such as ResNet50V2, Xception, VGG16, MobileNet, and InceptionV3, which were fine-tuned to classify railway track images into defective and non-defective categories. Additionally, the state-of-the-art YOLOv11 model was adapted for the same classification task, leveraging advanced data augmentation techniques to achieve high accuracy. Among the transfer learning models, VGG16 demonstrated the best performance with a test accuracy of 89.18%. However, YOLOv11 surpassed all models, achieving a test accuracy of 92.64% while maintaining significantly lower computational demands. These findings underscore the versatility of deep learning models and highlight the potential of YOLOv11 as an efficient and accurate solution for railway fault classification tasks.

The study of dynamic models and their parameterization remains a relevant topic in research. Motors and their models have been extensively analyzed, studied, and parameterized using various techniques due to their broad applicability in motorering and industrial settings. However, most methods for obtaining model parameters require at least two averaged signals from the motor, such as torque, current, speed, position, or acceleration. In this work, we propose the parameterization of a motor’s dynamic model using only the speed signal and the steady-state values of the variables. Through evolutionary computation, the mechanical and electrical equations of the motor are reconstructed based on this signal. This approach offers a significant advantage, as it enables parameter estimation without requiring the instrumentation needed for full current signal measurement or, alternatively, torque measurement. To achieve this, the transfer function representing the motor’s speed is utilized. The function reconstruction is performed with a Root Mean Square Error (RMSE) of less than 1% for both the speed and current signals. Since the original current signal is not required for this estimation, this work presents an innovative approach to estimating a system of dynamic equations using only a single measured variable and the dynamic relationships of its step-input response.

A thermal efficiency analysis of an organic Rankine cycle (ORC) system enables its performance to be evaluated; for this purpose, critical system components, including the turbine and the boiler, must be scrutinized. ORC plants can operate under various regimes, such as simple, regeneration, and reheat work modes. Organic fluids such as R-245fa integrate low-temperature sources such as solar radiation. However, a literature review revealed limited research on the impact of a solar collection system on the overall thermal efficiency of an ORC system during the regeneration stage. In this study, we examined the thermal efficiency behavior of an ORC plant with a 1 kW generator operating in simple and regeneration modes with a solar collection stage. The results show that the thermal efficiency in simple mode was 35.27%, while in regeneration mode with solar collection it reached 51.30%. Improving the thermal efficiency of a thermodynamic cycle system can reduce CO₂ emissions. The operating temperature ranges facilitate the development of a methodology for industries to implement ORC systems in their manufacturing processes, thereby utilizing waste heat from industrial operations.