Search Results (36)

By using a PMMA-made therapeutic wedge and a recently developed reusable silicone foil dosimeter based on the optically stimulated luminescence (OSL) of MgB₄O₇:Ce,Li (MBO) material, direct measurements of the complete proton Bragg curves for two independent clinically relevant proton beams were achieved. The PMMA wedge compensator created a controlled range gradient across the beam field, enabling comprehensive characterisation of Bragg curve features, including the entrance plateau, the maximum of the Bragg peak, and the dosimetrically critical distal fall-off region. Measurements were performed using a dedicated, self-built (3D-printed) optical detection setup equipped with a blue LED (440 nm) that illuminates the MBO foil dosimeter and a highly sensitive electron-multiplication (EMCCD) camera, which simultaneously acquires 2D OSL light from the foil. The prototype technology enables single-shot 2D mapping of the complete Bragg curve. Validation against Monte Carlo (MC) simulations and Gafchromic^TM EBT3 films demonstrates sub-millimetre accuracy in localising the clinically critical proton parameters: peak-to-plateau, FWHM and distal fall-off. Measurements were performed for two independent therapeutic proton beams with initial energies of 58.8 and 61.1 MeV, routinely used for proton eye-beam treatments at IFJ PAN Krakow. As a proof of concept, the results demonstrate the potential of MBO-based silicone foil technology to reproduce clinically relevant Bragg-curve parameters with accuracy approaching that of the current gold standard for passive 2D dosimetry, Gafchromic^TM EBT3 films, while systematic differences attributable to optical diffusion, residual LET-dependent quenching, and the dual-foil junction remain to be corrected. Full article

Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200–1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R² ≥ 0.999 across temperatures of 15–35 °C and humidity of 30–90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5–8 robots confirm map convergence times of 120–210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints. Full article

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article

Magnetic drug targeting (MDT) is commonly evaluated by peak accumulation at the target site. Under pulsatile flow, however, initial deposition does not predict sustained localisation. We introduce the Magnetic Targeting Optimisation Concept (M-TOC), a safety-constrained framework that restructures MDT evaluation by separating geometric deposition from retention stability and embedding both within a defined hemodynamic safety window. Deposition (D) was quantified by using obstruction degree at the injection end, OD(T0), and restricted by a structural admissibility limit (OD_max = 40%). Retention stability (R) was quantified using early washout at T0 + 30 s and an apparent half-life (τ_1/2) derived from coverage decay under controlled pulsatile washout. These descriptors were integrated into a Unified Targeting Score (UTS), applied only within the admissible domain, thereby enforcing feasibility before optimisation. Three PEG-functionalised magnetoresponsive nanocluster formulations were evaluated under identical magnetic and flow conditions. D–R mapping identified distinct operating regimes and showed that no tested configuration simultaneously achieved admissible deposition and robust pulsatile stability. By formalising MDT as a constrained multi-objective problem, M-TOC provides an objective method for regime discrimination and a transferable design principle for stability-guided targeting under physiological flow. Full article

The construction industry is increasingly adopting digital technologies to enhance productivity and efficiency, in alignment with the principles of Construction 4.0 (C4). The progress and advances recorded thus far are largely due to advancements in cyber-physical systems (CPS), computational processing power, deep learning solutions, robotics, and other related technologies. However, a major challenge in this research space is the lack of an integrated solution for both the interior and exterior construction environments, which has led to fragmented data, hindering efficiency. Several researchers have proposed frameworks in recent years that focused on either indoor or outdoor construction environments; this approach has resulted in the creation of siloed information, to the detriment of the C4 ideals and principles. In this study, a comprehensive system architecture for raw data captured using sensors and other inputs to provide useful insight for the construction team and stakeholders was mapped out. This study presents an integrated framework of various technologies for both indoor and outdoor construction environments. The solution provided for localisation algorithms and technologies such as Simultaneous Localisation and Mapping (SLAM), odometry, and inertial measurement unit (IMU) devices. The unified 5-level Cyber-Physical Systems (CPS) architecture was used as the primary architecture, and it was compared with the IoT Architecture layers in terms of data analytics and management perspectives. The Digital Twin (DT), which sits at the cyber level of the architecture, warehouses and tracks in real-time the dynamic complexities of the construction site throughout the project life cycle, serving as the single source of truth for the project. This system architecture and framework presented in this research contributed towards advancing the field of construction automation by offering a scalable solution for efficient construction in project management. Full article

Among the different activities of the AEC (Architecture, Engineering, and Construction) sector, the documentation phase is pivotal and covers the entire lifecycle of a building or infrastructure. In the last decade, in the geomatic field, technology has evolved rapidly, and several instruments and techniques have become available to assist operators in this documentation process. Furthermore, the AEC sector is moving toward the extensive use of Digital Twins, and the research presented in this paper focuses on the technological solutions available today for creating the metric and geometric base of the Digital Twin at the service of AEC sector. Geomatics instruments and techniques are widely adopted in this framework, particularly HMLS (Handheld Mobile Laser Scanner). This research will evaluate the differences in performances between the first and second generation of HMLS based on SLAM (Simultaneous Localisation and Mapping) technologies in terms of accuracy, precision, level of detail, data density, noise, and other relevant characteristics. To address the research questions of this work, it was decided to perform a series of tests in an ad hoc test field following predefined acquisition strategies and procedures. A series of analyses were then conducted on the processed data to evaluate several factors, particularly georeferencing of HMLS data, features analyses on specific areas, Cloud-to-Cloud analysis, and cross-sections analysis. Full article

A core problem relating to indoor positioning is a lack of prior knowledge of the environment. To date, most WiFi–RTT research assumes knowledge of the access points in an indoor environment. This paper provides a solution to this problem by using a simultaneous localisation and mapping (SLAM) algorithm, using WiFi–RTT and pedestrian dead reckoning, which uses the inertial sensors in a smartphone. A WiFi–RTT SLAM algorithm has only been researched in one instance at the time of writing; this paper aims to expand the exploration of this problem, particularly in relation to the use of outlier detection and motion models. For the trials, which were 35 steps long, the final mobile device horizontal positioning error was 1.01 m and 1.7 m for the forward and reverse trials, respectively. The results of this paper show that unmapped indoor positioning using WiFi–RTT is feasible for metre-level indoor positioning, given correct access point calibration. Full article

This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones. Full article

Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms. Full article

This work presents a novel RGB-D dynamic Simultaneous Localisation and Mapping (SLAM) method that improves the precision, stability, and efficiency of localisation while relying on lightweight deep learning in a dynamic environment compared to the traditional static feature-based visual SLAM algorithm. Based on ORB-SLAM3, the GCNv2-tiny network instead of the ORB method, improves the reliability of feature extraction and matching and the accuracy of position estimation; then, the semantic segmentation thread employs the lightweight YOLOv5s object detection algorithm based on the GSConv network combined with a depth image to determine potentially dynamic regions of the image. Finally, to guarantee that the static feature points are used for position estimation, dynamic probability is employed to determine the true dynamic feature points based on the optical flow, semantic labels, and the state in last frame. We have performed experiments on the TUM datasets to verify the feasibility of the algorithm. Compared with the classical dynamic visual SLAM algorithm, the experimental results demonstrate that the absolute trajectory error is greatly reduced in dynamic environments, and that the computing efficiency is improved by 31.54% compared with the real-time dynamic visual SLAM algorithm with close accuracy, demonstrating the superiority of DLD-SLAM in accuracy, stability, and efficiency. Full article

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D. Full article

The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains. Full article

This paper presents a warning that there is a need for better consideration of microclimatology in urban planning, particularly when addressing microclimate-related human comfort in designing outdoor public spaces. This paper develops a protocol for microclimate-related street assessment, considering simultaneous dynamic environmental components data gathering and better understanding of microclimatic conditions when commuting by bicycle. The development of new information and communication technologies (ICTs) has the potential for overcoming the gap between microclimatology and urban planning, since ICT tools can produce a variety of soft data related to environmental quality and microclimate conditions in outdoor spaces. Further, the interpretation of data in terms of their applicability values for urban planning needs to be well addressed. Accordingly, this paper tests one particular ICT tool, a prototype developed for microclimate data collection along cycling paths. Data collection was performed in two European cities: Bilbao (Spain) and Ljubljana (Slovenia), where the main objective was the development of a protocol for microclimate-related street assessment and exploration of the potential of the collected data for urban planning. The results suggest that the collected data enabled sufficient interpretation of detailed environmental data and led to a better consideration of microclimatology and the urban planning of cycling lanes. The paper contributes to urban planning by presenting a protocol and providing fine-grained localised data with precise spatial and temporal resolutions. The data collected are interpreted through human comfort parameters and can be linked with rates/levels of comfort. As the collected data are geopositioned, they can be presented on a map and provide links between environmental conditions within a spatial context. Full article

Three colour and depth (RGB-D) devices were compared, to assess the effect of depth image misalignment, resulting from simultaneous localisation and mapping (SLAM) error, due to forest structure complexity. Urban parkland (S1) was used to assess stem density, and understory vegetation (≤1.3 m) was assessed in native woodland (S2). Individual stem and continuous capture approaches were used, with stem diameter at breast height (DBH) estimated. Misalignment was present within point clouds; however, no significant differences in DBH were observed for stems captured at S1 with either approach (Kinect p = 0.16; iPad p = 0.27; Zed p = 0.79). Using continuous capture, the iPad was the only RGB-D device to maintain SLAM in all S2 plots. There was significant correlation between DBH error and surrounding understory vegetation with the Kinect device (p = 0.04). Conversely, there was no significant relationship between DBH error and understory vegetation for the iPad (p = 0.55) and Zed (p = 0.86). The iPad had the lowest DBH root-mean-square error (RMSE) across both individual stem (RMSE = $2.16$$\mathrm{c}$$\mathrm{m}$) and continuous (RMSE = $3.23$$\mathrm{c}$$\mathrm{m}$) capture approaches. The results suggest that the assessed RGB-D devices are more capable of operation within complex forest environments than previous generations. Full article

Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this study, an autonomous navigation robot platform named Owlbot was designed, which is equipped with a stepping motor as a mobile actuator. In addition, a stepping motor control algorithm was developed using polynomial equations, which can effectively convert speed instructions to generate control signals for accurately operating the motor. Using 2D LiDAR and an inertial measurement unit as the primary sensors, simultaneous localization, mapping, and autonomous navigation are realised based on the particle filtering mapping algorithm. The experimental results show that Owlbot can effectively map the unknown environment and realise autonomous navigation through the proposed control algorithm, with a maximum movement error being smaller than 0.015 m. Full article