Abstract
Monitoring the impacts of selective logging in tropical forests remains challenging due to the reliance on labor intensive field surveys. This study relies on the use of pre- and post-logging airborne LiDAR data to provide a precise and scalable method for quantifying canopy disturbances, carried out within the Sustainable Management Plan for the Jamari National Forest in Rond & ocirc;nia. The analysis of the airborne LiDAR data revealed a significant increase in canopy gaps after logging (F= 63.5,p <0.001 ), with canopy gaps corresponding to an average increase of 3.9 +/- 0.4% relative to the total plot area due to logging activities. The mean canopy gap area per felled tree was 158.29 m(2) ( +/- 35.7). A strong positive correlation was found between canopy gaps that emerged after logging and the logged AGB (18.4 +/- 1.7Mg ha(-1) ). A significant reduction in mean canopy height was also observed, decreasing from 26.26 +/- 0.40 m before logging to 24.62 +/- 0.33 m after logging (F= 9.86,p= 0.005) . The mean canopy gap area shifted from 40.68 +/- 2.30 m(2) to 77.07 +/- 2.82 m(2). Furthermore, there was an increase of 14.6% in the total number of gaps. The average Gini coefficient was 0.50 +/- 0.02 before logging and 0.64 +/- 0.01 in the post-logging areas and the average total impact on the canopy was 16.6 +/- 1.5% of the selectively logged area. The results obtained using the proposed methodology were consistent with field observations, demonstrating high accuracy of LiDAR-detected impacts when compared with inventory and GNSS data. This high detection rate highlights the sensitivity of LiDAR point cloud data in capturing small structural changes. Compared to pre-logging conditions, the observed alterations demonstrate that LiDAR provides a more precise and scalable approach for quantifying the impact of selective logging on forest structure.

Abstract
This paper describes an approach to algorithmic music composition that takes narrative structures as input, allowing composers to create music directly from narrative elements. Creating narrative development in music remains a challenging task in algorithmic composition. Our system addresses this by combining leitmotifs to represent characters, generative grammars for harmonic coherence, and evolutionary algorithms to align musical tension with narrative progression. The system operates at different scales, from overall plot structure to individual motifs, enabling both autonomous composition and co-creation with varying degrees of user control. Evaluation with compositions based on tales demonstrated the system's ability to compose music that supports narrative listening and aligns with its source narratives, while being perceived as familiar and enjoyable.

Abstract
This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings to harness wind power, similar to a kite, while maintaining VTOL functionality. A distinguishing feature is the purpose-built autopilot system, with custom hardware and software components specifically engineered for this application. The study presents the system identification process for obtaining five MIMO (Multiple-Input Multiple-Output) transfer functions that characterize the dynamics between roll-yaw commands and responses, including the tether angle feedback. To address the inherent coupling effects and uncertainties in the system, robust mixed sensitivity (H-infinity) MIMO controllers are developed. The controllers were validated through both simulations and experimental flights, demonstrating effective performance in handling cross-coupling effects and maintaining stability under various operating conditions. According to flight test findings, the system can precisely manage the tether angle while adjusting for ground effect disturbances. This allows for accurate tethered navigation, a stable attitude, and the maintenance of an adequate yaw heading.

Abstract
Capricious data streams, marked by random emergence and disappearance of features, are common in practical scenarios such as sensor networks. In existing research, they are mainly handled based on linear classifiers, feature correlation or ensemble of trees. There exist deficiencies such as limited learning capacity and high time cost. More importantly, the concept drift problem in them receives little attention. Therefore, drifting capricious data streams are focused on in this paper, and a new algorithm DCFHT (online learning from Drifting Capricious data streams with Flexible Hoeffding Tree) is proposed based on a single Hoeffding tree. DCFHT can achieve non-linear modeling and adaptation to drifts. First, DCFHT dynamically reuses and restructures the tree. The reusable information includes the tree structure and the information stored in each node. The restructuring process ensures that the Hoeffding tree dynamically aligns with the latest universal feature space. Second, DCFHT adapts to drifts in an informed way. When a drift is detected, DCFHT starts training a backup learner until it reaches the ability to replace the primary learner. Various experiments on 22 public and 15 synthetic datasets show that it is not only more accurate, but also maintains relatively low runtime on capricious data streams.

Abstract
Phishing attacks aims to steal sensitive information and, unfortunately, are becoming a common practice on the web. Email phishing is one of the most common types of attacks on the web and can have a big impact on individuals and enterprises. There is still a gap in prevention when it comes to detecting phishing emails, as new attacks are usually not detected. The goal of this work was to develop a model capable of identifying phishing emails based on machine learning approaches. The work was performed in collaboration with E-goi, a multi-channel marketing automation company. The data consisted of emails collected from the E-goi servers in the electronic mail format. The problem consisted of a classification problem with unbalanced classes, with the minority class corresponding to the phishing emails and having less than 1% of the total emails. Several models were evaluated after careful data selection and feature extraction based on the email content and the literature regarding these types of problems. Due to the imbalance present in the data, several sampling methods based on under-sampling techniques were tested to see their impact on the model's ability to detect phishing emails. The final model consisted of a neural network able to detect more than 80% of phishing emails without compromising the remaining emails sent by E-goi clients.

Abstract
This paper discusses some recent developments in collaborative healthcare research considering settings where human clinicians collaborate through or interact with artificial intelligence (AI)-enabled systems to enhance clinical diagnosis, treatment procedures, and decision-making practices. Through a detailed examination of the potential gaps, implications, and challenges for health professionals and patients, this work explores typical AI-based collaborative clinical workflows and infrastructures that involve tasks such as patient data analysis, medical imaging, and event prediction. A brief synopsis of published research reveals inherent sociotechnical barriers concerning interoperability, data scarcity, bias amplification, trust, and transparency. It also highlights risks related to inadequate model and interface design, the oversimplification of clinical processes (e.g., lack of shared situational awareness), institutional misalignment (e.g., cultural norms and practices shaping how clinicians coordinate their efforts and make decisions based on AI recommendations), and commercial data manipulation that threatens patient care. © 2025 IEEE.