Abstract
Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981-2018 (baseline period) and 2041-2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO2) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO2 fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO2 fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice.

Abstract
Automated fruit harvesting systems rely heavily on accurate visual perception, particularly for crops such as the Arbutus tree (Arbutus unedo), which holds both ecological and economic significance. However, this species poses considerable challenges for computer vision due to its dense foliage and the morphological variability of its berries across different ripening stages. Despite its importance, the Arbutus tree remains under-explored in the context of precision agriculture and robotic harvesting. This study addresses that gap by evaluating a computer vision-based approach to detect and classify Arbutus berries into three ripeness categories: green, yellow-orange, and red. A significant contribution of this work is the release of two fully annotated open-access datasets, Arbutus Berry Detection Dataset and Arbutus Berry Ripeness Level Detection Dataset, developed through a structured manual labeling process. Additionally, we benchmarked four YOLO architectures - YOLOv8n, YOLOv9t, YOLOv10n, and YOLO11n - as well as the RT-DETR models, using these datasets. Among these, RT-DETR-L demonstrated the most consistent performance in terms of precision, recall, and generalization, outperforming the lighter YOLO models in both speed and accuracy. This highlights RT-DETR's strong potential for deployment in real-time automated harvesting systems, where robust detection and efficient inference are critical. © 2025 IEEE.

Abstract

This document is intended to present a benchmark of multiple good practices in the context of internationalization studies, particularly focused on digital electronics and programmable devices, yet is not limited to them. This paper will start with a comprehensive paper desk analysis together with an in-depth research process that should lead to the selection of innovative tools applied to digital systems. International initiatives are oriented towards increasing the quality of higher education by motivating teachers of STEM disciplines to use a multidisciplinary approach and teach with the massive support of technologies like Classroom, MS-Teams, Blackboard, etc. The central goal is to suggest and recommend a model for integrating intermediate and advanced digital electronics subjects (e.g., FPGA, microcontrollers, etc.) and ICT in international teaching approaches such as Collaborative Online International Learning (COIL), Project-based Learning (PBL) and Real Remote Labs (RRL). This is the approach sought by the European Project DECEL.

Abstract
Teaching the processes of designing digital electronic systems is becoming an increasingly challenging task. Design methodologies and tools have evolved to cope with the ever-growing complexity and density, raising the abstraction level of the source design far away from the logic circuit. However, it is of paramount importance that fresh students start by understanding the fundamental concepts of Boolean algebra, design, and optimization of combinational and sequential gatelevel circuits, before moving to higher abstract concepts and tools. For this, hands-on practice with simple real digital circuits is essential to understanding the essentials of the operation of digital circuits and how digital data is propagated and transformed from block to block. In this paper we present a distributed infrastructure based on the network protocol MQTT to support the deployment of distributed digital systems built with parts located in different physical locations. Thus, promoting the implementation of collaborative online learning/teaching activities will be one of our main goals. Experimental results show latencies between remote sites in the range of a few tens of milliseconds, which is acceptable for running simple digital systems at low speeds, which is necessary for being perceived and understanded by people.

Abstract
The Digital electronics collaborative enhanced learning (DECEL) project has recently developed an international collaborative education course. Its main objective is to enhance the digital electronics skills of international students by working on a complex, multidisciplinary applied problem using a mixed digital architecture. We have developed a logic level synthesis and dedicated software layers on the Red Pitaya FPGA platform. The diversity of digital concepts to be implemented, from hardware description language (HDL) to high-level languages such as Python or Matlab, forced the students to work together and rapidly improve their skills. Their motivation was fueled by the curiosity of controlling an ultrasound probe to obtain ultrasound signatures. This particular physics, little known to the students, was an additional source of curiosity. The goal of forming an image in a liquid medium was an additional motivating factor for them. The students reported that they learned a lot from the experiment. Thus, the technical parts and pedagogical results are documented in this work for reproducibility.

Abstract
Human oversight can benefit scenarios with complex tasks, such as pallet docking and loading and unloading containers, beyond the current capabilities of autonomous systems without any failures. Furthermore, teleoperation systems allow remote control of mobile ground robots, especially with the surge of 5G technology that promises reliable and low latency communication. Current works research on exploring the latest features from the 5G standard, including ultra-Reliable Low-Latency Communication (uRLLC) and network slicing. However, these features may not be available depending on the Internet Service Provider (ISP) and communication devices. Thus, this work proposes a network architecture for the teleoperation of ground mobile robots in industrial environments using commercially available devices over the 5G Non-Standalone (NSA) standard. Experimental results include an evaluation of the network and End-to-End (E2E) latency of the proposed system. The results show that the proposed architecture enables teleoperation, achieving an average E2E latency of 347.19 ms.