Fungicide, also called antimycotic, is any chemical substance used to kill or inhibit fungal growth. Fungi, like humans, are eukaryotic organisms, and few selective targets can be exploited for the development of antifungal drugs. Consequently, the existing antifungal arsenal for treating fungal infections is limited, thus contributing to the high rates of morbidity and mortality currently observed. Additionally, the plant fungal pathogens have been developed resistance to the available fungicides, threatening crops productivity and the quality and safety of the foods. Mitochondria are double-membrane organelles that serve as the powerhouses of cells. Furthermore, mitochondria are still responsible for many other vital cellular processes, including apoptosis, maintenance of calcium homeostasis, heme biosynthesis, and cell homeostasis of iron, redox signaling, steroid synthesis, amino acids, and lipid metabolism. Many of the commercially successful fungicides act as inhibitors of mitochondrial energy metabolism. This effect is achieved by specific interactions with proteins in the electron transport chain and non-specific interactions with the membrane. However, mitochondria have several highly conserved components, so it has been a tremendous challenge to develop fungicides that are non-toxic to non-target organisms. Fungicidal residues have contaminated our food and environment. Although these compounds are present in very low concentrations, their long-term effects on human health are of concern. It is well known that disruption of mitochondrial metabolism caused by fungicides results in dangerous disturbances in target and non-target organisms such as fish, insects, plants, and humans.
Deep Reinforcement Learning (DRL) has achieved remarkable results in simulated environments for its ability to learn optimal actions through interaction. Yet, applying these techniques to complex urban contexts remains challenging due to the need for efficient exploration and adaptive decision-making. This work applies DRL to autonomous navigation in a realistic simulated environment based on Vila Real, Portugal, comparing the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms. Alongside their implementation, the mathematical principles underlying both methods are examined to clarify their learning dynamics. The PPO agent achieved a best cumulative reward of 69.37 after 150 million training steps (66.1 hours), while SAC obtained -7.91 after 182.7 hours, showing limited performance. These results demonstrate the effectiveness of PPO-based DRL for autonomous navigation in complex urban environments.
We prove local Hölder continuity for nonnegative, locally bounded, local weak solutions to the class of doubly nonlinear parabolic equations ∂ _t (u^q)- div(| Du| ^p-2 Du) = 0, p>2, 0
In modern industrial environments, ensuring the quality of manufactured components is critical, particularly when dealing with reflective surfaces that hinder conventional inspection techniques. Although deep learning-based methods offer robust solutions for visual defect detection, their performance often hinges on the availability of substantial annotated datasets. In industrial scenarios, labeling such datasets is costly and time-consuming. This study investigates applying sample selection techniques to reduce annotation efforts for porosity detection on machined aluminium parts. Several selection strategies were evaluated using a real-world dataset composed of high-resolution images, including uncertainty, diversity, random-based criteria, and hybrid combinations. The best-performing strategy, which combined entropy-based uncertainty, spatial diversity, and random-based, achieved an F1-score of 86.70
The development of computational thinking in introductory programming remains a major challenge, particularly in vocational secondary education, where students frequently encounter difficulties in understanding code structure, identifying errors, and translating problem statements into algorithmic solutions. This study examines the use of code comments as a pedagogical scaffold within a digitally supported learning environment enriched with multimodal instructional resources for novice programming students. An exploratory mixed-methods classroom intervention was conducted in a vocational secondary education context, combining comment-centered coding activities with digital educational resources such as online forms, classroom management tools, guided worksheets, interactive exercises, and feedback-oriented learning tasks. The intervention was examined through a pre- and post-test on computational thinking, a pre- and post-intervention questionnaire on students’ knowledge, practices, and perceptions regarding code comments, and supporting qualitative evidence from classroom artifacts. The findings showed a marked improvement in computational thinking performance over the course of the intervention, with the mean test score increasing from 43% to 91%. Students also reported more positive and more elaborated views regarding the role of code comments in code comprehension, debugging, organization, and learning support. Given the single-group design and small sample, these results should be interpreted as exploratory and context-specific rather than as evidence of intervention-only causality. Nevertheless, the study provides practice-based evidence that code comments, when embedded in a structured multimedia-supported learning design, may function as a useful pedagogical scaffold in introductory programming education. This study contributes empirical evidence from an underrepresented vocational secondary education setting and offers actionable implications for multimedia-based digital learning in computer science education.
