Abstract
Scrum was originally projected for environments with small teams working in the same place, where collaboration and physical proximity are key for success. Accordingly, it becomes relevant to explore how scrum can be implemented in geographically distributed teams. This study aims to identify a set of different types of practical distributed scrum implementation using three case studies with Portuguese software companies. Furthermore, it explores the main motivations for this migration, the challenges posed by the geographical dispersion of teams, and the benefits brought by this approach to organisations. The findings reveal three approaches for implementing distributed scrum considering the geographical location of the employees and the challenges that are posed in terms of communication, collaboration and coordination. These approaches enhance the theoretical knowledge in the field and help software companies to migrate from traditional scrum environments to large-scale distributed environments. Copyright © 2024 Inderscience Enterprises Ltd.

Abstract
The utilization of unmanned vehicles for specialized tasks has gained significant attention in both military and civilian domains. This article explores the application of commercial unmanned aerial vehicles (UAVs) for reconnaissance purposes, specifically to verify autonomous driving missions assigned to the developed TAERO manned-unmanned vehicle in field operations. The paper introduces the TAERO vehicle, highlighting its functionality and capabilities for unmanned missions. The architecture of the unmanned ground vehicle (UGV) system is discussed taking into consideration the autonomy subsystem and used location data. The limitations associated with terrain and potential obstacles are addressed as well as importance of acquiring accurate terrain information for successful autonomous operation. The solution proposed in our study involves the use of a commercially available UAV applied to the visual tracking of potential targets in an engagement scenario. Details related to flight route planning system, geolocation, target tracking, and data transmission between robotic platforms are discussed and presented in this work. The acquired real-time data plays a crucial role in confirm- ing the mission, making necessary adjustments, or altering the planned route. The UAV platform, known for its maneuverability and operational capabilities, can operate ahead as a reconnaissance element, improving the overall reconnaissance capabilities of the system. Upon completion of the mission, the UAV can return to the base or land on a moving vehicle platform. The authors proposed integration of a UAV that significantly enhances the autonomous mode capabilities of unmanned ground platform, improving operation in unknown environment during special mission.

Abstract
Turbidity and suspended sediment concentration are crucial parameters indicative of water quality, playing pivotal roles in evaluating the well-being of aquatic ecosystems and the effectiveness of water treatment processes. This manuscript provides an in-depth review of various methods and instruments in use for in situ and inline applications. The exploration of optical instrumentation is central to this review, examining its widespread use and current challenges within standard methods, commercial instruments and scientific research. The study also delves into alternative techniques, such as acoustic and capacitive methods, elucidating their applications, calibration intricacies, and practical considerations. Furthermore, the paper scrutinizes the emerging importance of satellite and aerial imaging processing as a supplementary tool for turbidity monitoring, underscoring its potential to offer comprehensive insights on a larger scale. The review emphasizes the key accomplishments and challenges of the state-of-the-art technologies, providing a comprehensive overview of the current stage of the field and its prospects. and aims to provide valuable insights for researchers, practitioners, and decision-makers involved in environmental monitoring and water facility management, enabling a deeper comprehension of the significance of turbidity and suspended sediment concentration in safeguarding water quality and ecosystem health.

Abstract
The gold standard methods for real-time core temperature (CT) monitoring are invasive and cost-inefficient. The application of Kalman filters for an indirect estimation of CT has been explored in the literature since 2010. This paper presents a comparative study between different state of the art Extended Kalman Filter (EKF) estimation algorithms and a new approach based on a biomimetic human body response pre-emptive mapping concept. In this new method, a mapping model of the physiological response of the heart rate (HR) change to CT increase is pre-applied to the input of the EKF estimation CT procedure in a near real-time manner. The algorithm was trained and tested using two datasets (total participants = 18). The best performing algorithm with this novel pre-emptive mapping achieved in an average Root Mean Squared Error (RMSE) of 0.34°C while the best state of the art EKF model (without pre-emptive mapping) resulted in a RMSE of 0.41°C, leading to a 17% improvement performance of our novel method. Given these favorable outcomes, it is compelling to assess its efficacy on a larger dataset in the near future.

Abstract
This work focuses on the application of image processing techniques to segment and analyze images of brain sections with the aim of facilitating early diagnosis of brain tumors. The aim is to delineate specific regions of the brain, such as the cranial, intracranial, and encephalic regions, for subsequent geometric analysis. The process involves image pre-processing, conversion to polar coordinates, determination of contour points, Fourier Series approximation, and the use of the Least Square Method to obtain accurate representations of the regions. The proposed approach was tested on Magnetic Resonance Images of three different brains, showing its capability to accurately delineating the targeted regions. The results highlight the potential of signal processing techniques for analyzing brain images and provide insights for further research in this area.

Abstract
This article presents the development of a low-cost 3D mapping technology for trajectory planning using a 2D LiDAR and a stepper motor. The research covers the design and implementation of a circuit board to connect and control all components, including the LiDAR and motor. In addition, a 3D printed support structure was developed to connect the LiDAR to the motor shaft. System data acquisition and processing are addressed, as well as the generation of the point cloud and the application of the A* algorithm for trajectory planning. Experimental results demonstrate the effectiveness and feasibility of the proposed technology for low-cost 3D mapping and trajectory planning applications.