The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.

As the PHY/MAC-layer IR-HARQ and RLC-layer ARQ error recovery procedures, adopted in LTE, may impose additional delay when their code-block retransmissions occur, the arising question is whether these significantly contribute to IP and consequently RTP packet delays, and finally degrade the overall application-layer end-to-end QoE, especially when voice is transmitted over LTE? With this regard, we propose and demonstrate a VoLTE QoS and QoE test procedure based on PHY/MAC/RLC/IP/TCP-UDP/RTP cross-layer protocol analysis and perceptual speech quality QoE measurements. We identified monotonic relationship between the paired observations: QoE and HARQ RTT, i.e. between the PESQ voice quality rating and the IP/RTP packet latency, for given BLER of the received MAC/RLC code-blocks. Specifically, we found out that, for the HARQ RTT value of about 8 ms, only up to 2 HARQ retransmissions (and consequently no RLC-ARQ one) is appropriate during any voice packet, otherwise delay accumulation might not be accordingly “smoothed out” by jitter/playback buffers along the propagation path.

Personalized learning has gained significant attention in recent years in response to the limitations of one-size-fits-all approaches to teaching, particularly in areas such as programming education where learners vary widely in prior knowledge, pace and learning preferences. The Python programming language has recently become the language of choice for introductory programming courses thanks to its simple syntax and broad applicability. However, even with a beginner-friendly programming language such as Python, learners benefit most when instruction is thoughtfully organized and flexible enough to meet their diverse needs. This paper presents EduPythontology, an educational ontology specifically developed to support the teaching and learning of Python through a semantically structured knowledge representation. The ontology formalizes programming concepts and their instructional relationships by capturing both logical dependencies and pedagogically motivated sequences based on established educational theories and research findings. This, in turn, enables the design of adaptive learning paths. In addition, EduPythontology combines instructional design components related to learning goals, cognitive complexity, learner preferences and resource types to facilitate the personalization of learning paths. During the development of the ontology, we followed the METHONTOLOGY framework as our main methodological reference, though some phases were slightly adapted to better match the goals of this work. The ontology was developed through iterative cycles of concept definition, relation refinement and validation. Logical consistency was verified using automated reasoning, and competency questions confirmed that the model met its purpose. Expert review led to minor revisions that improved clarity. The final version proved to be clear and consistent. It offers a dependable base for further work on intelligent tutoring tools in Python education.