Abstract
The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. While extensions of the Very Fast Decision Tree (VFDT) remain state-of-the-art for tabular stream mining, their unregulated growth limit efficiency, particularly in ensemble settings where post-pruning at the individual tree level is seldom applied. This paper presents DFDT, a novel memory-constrained algorithm for online learning. DFDT employs activity-aware pre-pruning, dynamically adjusting splitting criteria based on leaf node activity: low-activity nodes are deactivated to conserve resources, moderately active nodes split under stricter conditions, and highly active nodes leverage a skipping mechanism for accelerated growth. Additionally, adaptive grace periods and tie thresholds allow DFDT to modulate splitting decisions based on observed data variability, enhancing the accu-racy–memory–runtime trade-off while minimizing the need for hyperparameter tuning. An ablation study reveals three DFDT variants suited to different resource profiles. Fully compatible with existing ensemble frameworks, DFDT provides a drop-in alternative to standard VFDT-based learners. © 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Abstract
This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation.

Abstract
Recent technological advancements have increased computer crime, requiring public authorities to implement structured mitigation strategies. While initiatives exist to improve digital literacy on device security, they must also address the complexities of computer crime. Using Design Science Research, this study investigated the applicability of chatbots to raise awareness of computer crime in a public administration setting. A systematic literature review highlighted the issue's relevance and identified knowledge gaps. A scoping review gathered concepts, methodologies, technologies, architectures, and tools for developing and evaluating an effective chatbot. The design and development phase included a detailed proposal for a sophisticated chatbot architecture. During the demonstration and evaluation phases, the utility of the chatbot was tested in the domain of conversational flow efficiency and usability. The study's primary results and contributions are to assess the chatbot's effectiveness in raising awareness of computer crime on public websites. Future work should focus on implementing the chatbot in the actual context of public administration, proposing a network of specialized conversational assistants, and improving public service interoperability to enhance computer crime awareness.

Abstract
The transition from centralized to distributed control architectures in mobile robotics introduces the challenge of "Temporal Coherence"- the requirement that physically separated actuators execute commands simultaneously. This paper presents a validation of standard Ethernet Multicast to achieve Soft Real-Time synchronization suitable for dynamic locomotion. An "Inertial Filtering Condition"is proposed, positing that if network jitter (sc) is negligible relative to the robot's mechanical time constant (tmech), the system becomes functionally deterministic. Using a Dual-Core SMP architecture on RP2350 microcontrollers, this study demonstrates that standard Layer 2 Ethernet switches serve as effective hardware synchronizers. Experimental results show a packet-arrival reliability of 99.92% for a 1 kHz control loop and a network jitter standard deviation of s ˜65 µs. Furthermore, a Virtual Hardware-in-the-Loop (HIL) stress test confirmed that inter-leg actuation skew is reduced to ˜ 275 µs, representing a 32x improvement over the 9 ms latency bottleneck observed in previous USB-Serial architectures. Finally, it is concluded that standard Ethernet transport provides sufficient statistical determinism for coordinated gait stability without requiring complex hard real-time protocols.

Abstract
Evaluating diffusion properties of novel optical clearing (OC) agents is critical for advancing medical imaging. Tartrazine (TTZ), a strong absorbing dye, has shown promise in enhancing tissue transparency, yet its diffusion properties remain uncharacterized. In this work, OC treatments with TTZ-water solutions with varying osmolarities were performed, and the diffusion times (tau) that characterize the tissue dehydration and the RI matching mechanisms were estimated. From kinetic T-c measurements during treatment, tau values of water and TTZ were estimated in muscles as 60.0 s and 416.0 s, respectively. Corresponding diffusion coefficients (D) were derived from sample thickness data measured during treatments where the unique fluxes of TTZ and water occur. The respective D values were then calculated as 1.9 x 10(-6) cm(2)/s for water and 3.6 x 10(-7) cm(2)/s for TTZ. These findings provide key insights into TTZ diffusion in skeletal muscle and support its potential as an effective OC agent.

Abstract
Kidney paired donation (KPD) programs are organized in various countries to facilitate the donation of kidneys from willing but incompatible donors by matching them with pairs in similar situations. These programs often struggle with an accumulation of difficult-to-match recipients and small pools of incompatible pairs. To address this, several international collaborations have emerged to expand the pool sizes and increase the number of transplants by exchanging donors' kidneys across countries. We identified 4 established international KPD programs, each supported by protocols and agreements signed by the participating parties. Each program is presented separately, detailing its historical establishment, operational aspects, and statistics on pool characteristics and performance. Following this, we provide a comparative analysis of key aspects across the 4 programs. Each program has its unique context and specificities. Even though 3 of 4 collaborations started just before the COVID-19 pandemic, they have collectively facilitated >450 transplants. This underscores the importance of further developing these collaborations to share practices and experiences, and to facilitate more transplants, particularly for difficult-to-match recipients. Three of the 4 presented collaborations are either fully operated or led by European countries. This highlights the crucial role of ongoing international cooperation in the development of KPDs, in particular in Europe. By further promoting collaboration among countries, we can facilitate pan-European exchanges and improve access to live kidney transplants for patients in need.