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This article considers how increased access to communications technology could improve the lives of Indigenous peoples in Quebec. The authors describe the digital divide between Indigenous and non- Indigenous peoples in Quebec with respect to the speed, reliability, and cost of communications infrastructure, and how existing barriers can be overcome. The authors describe some ways in which the use of technology has already been incorporated by Indigenous communities to support education and healthcare, and how bridging the digital divide can be a tool to increase access to these fundamental services. Although technology is not a panacea, it has the potential, if implemented in accordance with Indigenous values, traditions, and goals, to empower Indigenous communities, particularly those in remote regions of Quebec, and alleviate some of the difficulties associated with accessing education and healthcare. [ABSTRACT FROM AUTHOR]

The paper aims at revealing main particularities of new technologies in connection with the development of health services. The development of information technology and telecommunications and as well the new medical attendance technologies are concerned. The study starts at the global healthcare level, with respect to quality and productivity in health services and then it is directed towards highlighting investments in medical technical equipment made in our country in recent years, by analyzing statistical data and forecasts of the need to introduce in Romanian hospitals certain advanced diagnostic and treatment equipment, through programs proposed by Ministry of Health. The main expected outcomes refer to deepening knowledge concerning the actual state of the new technologies in healthcare in connection with productivity growth in Romania in the context of globalization and highlighting the main ways of action for developing the Romanian health services under the influences of the new technologies. [ABSTRACT FROM AUTHOR]

Large-sample datasets containing hydrometeorological time series and catchment attributes for hundreds of catchments in a country, many of them known as “CAMELS” (Catchment Attributes and MEteorology for Large-sample Studies), have revolutionized hydrological modelling and have enabled comparative analyses. The Caravan dataset is a compilation of several (CAMELS and other) large-sample datasets with uniform attribute names and data structures. This simplifies large-sample hydrology across regions, continents, or the globe. However, the use of the Caravan dataset instead of the original CAMELS or other large-sample datasets may affect model results and the conclusions derived thereof. For the Caravan dataset, the meteorological forcing data are based on ERA5-Land reanalysis data. Here, we describe the differences between the original precipitation, temperature, and potential evapotranspiration (Epot) data for 1252 catchments in the CAMELS-US, CAMELS-BR, and CAMELS-GB datasets and the forcing data for these catchments in the Caravan dataset. The Epot in the Caravan dataset is unrealistically high for many catchments, but there are, unsurprisingly, also considerable differences in the precipitation data. We show that the use of the forcing data from the Caravan dataset impairs hydrological model calibration for the vast majority of catchments; i.e. there is a drop in the calibration performance when using the forcing data from the Caravan dataset compared to the original CAMELS datasets. This drop is mainly due to the differences in the precipitation data. Therefore, we suggest extending the Caravan dataset with the forcing data included in the original CAMELS datasets wherever possible so that users can choose which forcing data they want to use or at least indicating clearly that the forcing data in Caravan come with a data quality loss and that using the original datasets is recommended. Moreover, we suggest not using the Epot data (and derived catchment attributes, such as the aridity index) from the Caravan dataset and instead recommend that these should be replaced with (or based on) alternative Epot estimates.

Autonomous driving offers benefits such as congestion mitigation, increased productivity through the reallocation of driving time, and decreased energy waste. However, achieving Level 4 and 5 autonomous driving remains a significant challenge for both academia and industry. Among the various modules of autonomous driving, High-Definition (HD) maps have become a crucial component due to their high precision in map elements, enabling accurate localization, scene interpretation, navigation, vehicle control and motion forecasting of trajectory of surrounding objects. Several map providers, including TomTom, HERE, Waymo, and NVIDIA, create HD maps for their specific purposes. However, most HD map datasets are not publicly available for individual researchers and companies to investigate the current trends in HD map generation. Furthermore, recent survey papers on HD map generation have tended to focus only on specific aspects, such as road topology or boundary extraction, rather than considering the overall end-to-end HD map generation process. Therefore, we begin with a brief definition, standards, and functionality of HD maps, followed by an exploration of different types of HD maps, including offline and online variants, highlighting their respective advantages and disadvantages. Finally, we will discuss the most recent end-to-end HD map generation architectures, along with various types of open-source HD map datasets and compare their performances.

Accurate surveying of vegetated areas presents significant challenges due to obstructions that obscure visibility and compromise the precision of measurements. This paper introduces a methodology employing the DJI Zenmuse L2 Light Detection and Ranging (LiDAR) sensor, which is mounted on a Matrice 350 RTK drone. The DJI Zenmuse L2 sensor excels at capturing detailed terrain data under heavy foliage, capable of collecting 1.2 million points per second and offering five returns, thus enhancing the sensor's ability to detect multiple surface responses from a single laser pulse. In a case study conducted near a creek heavily obscured by tree coverage, traditional aerial imaging techniques are found insufficient for capturing critical topographic features, such as the creek banks. Employing LiDAR, the study aims to map these obscured features effectively. The collected data is processed using DJI Terra software, which supports the accurate projection and analysis of the LiDAR data. To validate the accuracy of the data collected from the LiDAR sensor, traditional survey methods are deployed to ground truth the data and provide an accuracy assessment. Ground control points (GCPs) are established using a GNSS receiver to provide geodetic coordinates, which then assist in setting up a total station. This total station measures vertical and horizontal angles, as well as the slope distance from the instrument to positions underneath the tree coverage on the ground. These measurements serve as checkpoints to validate the accuracy of the LiDAR data, thus ensuring the reliability of the survey. This paper discusses the potential of integrating LiDAR data with traditional surveying data, which is expected to enhance the ability of surveyors to map environmental features efficiently and accurately in complex and vegetated terrains. Through detailed procedural descriptions and expected outcomes, the study aims to provide valuable insights into the strategic application of geospatial technologies to overcome common surveying challenges.

This research introduces a novel approach to improve vision-based positioning in the absence of GNSS signals. Specifically, we address the challenge posed by obstacles that alter image information or features, making retrieving the query image from the database difficult. While the Bag of Visual Words (BoVW) is a widely used image retrieval technique, it has a limitation in representing each image with a single histogram vector or vocabulary of visual words, i.e., the emergence of obstacles can introduce new features to the query image, resulting in different visual words. Our study overcomes this limitation by clustering the features of each image using the k-means method and generating a graph for each class. Each node or key point in the graph obtains additional information from its direct neighbors using functions employed in graph neural networks, functioning as a feedforward network with constant parameters. This process generates new embedding nodes, and eventually, global pooling is applied to produce one vector for each graph, representing each image with graph vectors based on objects or feature classes. As a result, each image is represented with graph vectors based on objects or feature classes. In the presence of obstacles covering one or more graphs, there is sufficient information from the query image to retrieve the most relevant image from the database. Our approach was applied to indoor positioning applications, with the database collected in Bolz Hall at The Ohio State University. Traditional BoVW techniques struggle to properly retrieve most query images from the database due to obstacles like humans or recently deployed objects that alter image features. In contrast, our approach has shown progress in image retrieval by representing each image with multiple graph vectors, depending on the number of objects in the image. This helps prevent or mitigate changes in image features caused by obstacles covering or adding features to the image, as demonstrated in the results.

The aim of this work is to develop an approach that enables Unmanned Aerial System (UAS) to efficiently learn to navigate in large-scale urban environments and transfer their acquired expertise to novel environments. To achieve this, we propose a metacurriculum training scheme. First, meta-training allows the agent to learn a master policy to generalize across tasks. The resulting model is then fine-tuned on the downstream tasks. We organize the training curriculum in a hierarchical manner such that the agent is guided from coarse to fine towards the target task. In addition, we introduce Incremental Self-Adaptive Reinforcement learning (ISAR), an algorithm that combines the ideas of incremental learning and meta-reinforcement learning (MRL). In contrast to traditional reinforcement learning (RL), which focuses on acquiring a policy for a specific task, MRL aims to learn a policy with fast transfer ability to novel tasks. However, the MRL training process is time consuming, whereas our proposed ISAR algorithm achieves faster convergence than the conventional MRL algorithm. We evaluate the proposed methodologies in simulated environments and demonstrate that using this training philosophy in conjunction with the ISAR algorithm significantly improves the convergence speed for navigation in large-scale cities and the adaptation proficiency in novel environments. The project page is publicly available at https://superhan2611.github.io/isar_nav/.

Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

The availability of Global Positioning System (GPS), or more in general of Global Navigation Satellite Systems (GNSS), and the development of smart mobile devices, able to exploit the geospatial information provided by GPS/GNSS and integrate their use within many applications, have had a dramatic impact on the everyday life of most of the World population. While GNSS allows for real-time positioning in a wide range of scenarios, there are many challenging environments, such as tunnels and urban canyons, where GNSS-based solutions are inaccurate, unreliable, or even unavailable. The enormous interest in applications requiring ubiquitous positioning (e.g., self-driving vehicles) has been motivating the development of alternative positioning systems to support or substitute GNSS when operating in challenging scenarios. Alternative positioning systems to GNSS are usually developed by employing several sensors, such as radio-based, vision, LiDAR (Light Detection and Ranging), and RADAR (Radio Detection and Ranging). Furthermore, a collaborative approach can also be developed to increase the robustness of the navigation solution of inter-connected vehicles. To support research in this area, we are presenting the CONTEST (Collaborative pOsitioning and NavigaTion bEtween ground and uaS plaTforms) dataset, aiming at providing multiple data streams to test collaborative positioning approaches, involving both terrestrial and aerial platforms, based on the use of several sensors, such as Ultra-Wide Band (UWB) transceivers, cameras, LiDARs, GNSS. Data are described and some initial results presented.

Despite GNSS (Global Navigation Satellite System) enables positioning, navigation and timing (PNT) almost everywhere, the development of applications like self-driving vehicles and indoor navigation requires extending accurate positioning to scenarios where GNSS either is not reliable or does garantee a sufficiently precise solution. Integrating inforamtion provided by different sensors is commonly accepted to be a quite viable way for such extension. In particular this work is part of a project aiming at investigating the positioning performance that can be obtained by integrating vision with radio-based systems and inertial sensors, which are commonly installed on many smart devices, such as smartphones. Furthermore, this work considers positioning in a collaborative scenario, where different interconnected platforms, i.e. unmanned aerial vehicles and pedestrians provided with smartphones, are moving on the same area. The results obtained in the considered tests show a good potential (submetric 2D positioning error) for what concerns the implemented strategies, where the integration of different technologies can ensure decent performance in a wider range of working cases.

Abstract Distillation serves as the foremost method for commercial-scale separation of fluid mixtures. Widely applied in wastewater treatment, it is the preferred choice for isolating volatile multi-component mixtures into pure substances. Distillation technology offers notable economic benefits due to its easy implementation, high efficiency, productivity, and robust safety features. This study examines the environmental impacts associated with the production and usage of a distillation, specifically in treating pharmaceutical process wastewater containing organic halogen compounds (AOX). The analysis adopts a 'gate-to-gate' approach, with the specified functional unit (FU) set at 1 kg of treated effluent containing no more than 8 ppm of AOX and less than 1000 mg O2/L of Chemical Oxygen Demand (COD). In this work, Life Cycle Assessment (LCA) is conducted using Product Environmental Footprint (PEF) and Recipe 2016 Endpoint (H) V1.06 methodologies, utilizing the SimaPro V9.3.0.3 software in conjunction with the Ecoinvent V3.8 database. Analysis results have shown the emission of 1.11 × 10–2 kg CO2-eq, in which operational and production processes contribute 91.9% and 8.1%, respectively. To mitigate adverse effects, alternative energy sources, i.e., solar, offshore wind, and onshore wind are integrated into the distillation procedure. The substitution of hard coal with solar, offshore wind, and onshore wind energy displays the potential to significantly reduce climate change impact by 64.3%, 62.9%, and 62.8%, respectively. Article Highlights Distillation process undergoes a thorough life cycle assessment from production to application. Distillation process requires high energy and emits 1.11 × 10–2 kg CO2-eq per functional unit. The operational phase dominates over 90% in three damage categories: human health, ecosystems, and resources.

Digital documentation of historical sites has always required the use of expensive professional grade sensors capable of collecting large amounts of data to reconstruct cultural sites. These types of projects generally require large budgets and a large team of specialists to successfully generate a digital model. However, with smart devices having sensors capable of mapping on the go, the potential for mapping such historical sites may be more accessible. This study aims to conduct a comprehensive comparison between the iPhone 13 Pro and the Unmanned Aerial Systems (UAS) photogrammetric model of the Great Pyramid of Giza, otherwise known as the Khufu pyramid, located in Giza, Egypt. The purpose of this study is to evaluate the potential of the iPhone 13 Pro's Camera and LiDAR sensor capabilities as a valuable tool for documenting and preserving cultural heritage sites. To accomplish this, data was captured from multiple positions around the pyramid using the Pix4Dcatch app on the iPhone 13 Pro, and the data was processed using Pix4Dmatic to generate a 3D point cloud of the pyramid. This point cloud data is then compared to the reference data obtained through the UAS mapping which generated a 3D photogrammetric model. The comparison aims to identify the strengths and weaknesses of using the iPhone 13 Pro for this type of scanning and to assess the accuracy and precision of the generated data.

In this study, we investigate the feasibility of using an iPhone 14 Pro's camera and LiDAR sensors to collect high-accuracy spatial data on a mobile e-scooter. Given the widespread availability of e-scooters in urban areas, they present an ideal platform for creating a compact mobile mapping system. The iPhone is securely mounted on the e-scooter and paired with a viDoc RTK Rover, which offers real-time kinematic (RTK) positioning accuracy in open sky areas. As the e-scooter traverses the area of interest, data is collected using the LiDAR sensor, while images are captured using the camera. The collected data is then processed using Pix4Dmatic software, enabling the generation of a fused point cloud and a detailed digital model of the surveyed area. In situations where the Global Navigation Satellite System (GNSS) signal is compromised or unavailable, such as indoor environments or urban canyons, alternative methods like Simultaneous Localization and Mapping (SLAM) can be employed. Additionally, Total Stations can be utilized to track the entire system's movement in GNSS-denied environments and provide accurate georeferencing for the acquired data. Control and check points throughout the area of interest are established using the Total Station as well. This approach offers a flexible and cost-effective means of collecting high-accuracy spatial data in small areas across a variety of environments, leveraging the readily available e-scooters for public use. The results of various experiments conducted using an iPhone 14 Pro and viDoc RTK on an e-scooter are thoroughly analyzed and reported in this paper.

Abstract The paper focuses on energy modelling that involves a concatenated structure of a linear time‐invariant system. A block‐structured (BS) technique was adopted for a nonlinear system identification. Using the superimposition principle, the model mapped the thermodynamic state variables as an indirect function of time to the output function. The available energy and degradation of solar radiation are determined through a black box model. For testing and validation purposes, a solar collector with recirculating air was considered. The basic principle is to establish a relation between state variables and the performance parameters, without invoking the conventional thermodynamic relationship between them. The output of the model was compared with the validation data to ensure whether or not there was any affinity between them. The sigmoidal, wavenet, and polynomial forms of nonlinearity provided a good fit to the experimental dataset. The mean absolute percentage error encountered while estimating the collector efficiency was noticed to vary from −4.85 × 10−03% to 1.22 × 10−03%. Similarly, it falls in the domain of −4.73 × 10−04% to 7.78 × 10−02% for the second law efficiency. The maximum heat loss rate (BS model) obtained across the first and second passages of the solar air collector was 235.41 and 218.19 W at the air mass flow rate of 8.10 g/s, which is congruent to the validation dataset.

Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.

Airborne and ground mobile mapping platforms are increasingly used in group formations to increase productivity or complement each other in terms of improving observation capacity and efficiency in the surveyed area. Similarly, the navigation of assisted and autonomous vehicles presents a similar problem of sharing a small place where, for example, accurate relative positioning is essential to avoid collisions. Estimating platform relative positions from inter-platform range measurements is important in these applications, as they can provide valuable information to improve individual platform navigation or can potentially detect anomalies in these solutions that could be caused by unintentional or intentional disturbances. Free-network adjustment based on ranges forms the baseline solution to obtain relative positions. The challenge is to provide adequate platform position approximations for the least squares adjustment to achieve both quick convergence and fast execution time. Here we propose a preprocessing method that creates suitable approximations based on range values.

The probabilistic technique was used to optimize the torrefaction parameters that indirectly influence the yield of end-products obtained through the pyrolysis of biomass. In the same pursuit, pine cones underwent thermal pre-treatment at 210 °C, 220 °C, 230 °C, 240 °C, and 250 °C in the presence of N2 gas with a flowing rate of 0.7 L∙s−1, whereas the duration of the pre-treatment process was 5 min, 10 min, and 15 min at each. To facilitate the processing of pine waste, a muffle furnace was improvised for pilot-scale testing. The thermal process used to carry out torrefaction was quasi-static. The average dynamic head of volatile gases inside the chamber was 1.04 m. The criteria for determining the optimal solution were based on calorific value, solid yield, energy consumption during the pre-treatment process, and ash handling. In absolute terms, time and temperature did not influence the statistical deviation in cellulose and hemicellulose decomposition after thermal pre-treatment. While considering ash content as a primal factor, thermal processing should be conducted for 5 min at 210 °C for the bounded operating conditions, which are similar to the operating conditions obtained experimentally. The optimal solid yield would be obtained if the thermal pre-treatment is performed at 250 °C for 5 min. The solution derived through a simulated annealing technique provided a better convergence with the experimental dataset.

Reclaimed asphalt pavement (RAP) is gained from road reconstructions; however, its usage is less optimised in Hungary and neighbouring regions, since on the project level the proportion of RAP in the asphalt mixes is only 10-15%. This is less than the recommended level in other EU countries. The higher usage of RAP provides economic and environmental advantages, decreasing the need for new materials, the transport cost, and the carbon footprint. The composition of the resultant bituminous binder blend is a critical element in the asphalt mix design with high RA content. This paper discusses the design of the resultant bituminous binder blend to provide performance and compliance characterisation. This paper also presents the complex rheological analysis of the base bitumen, the bitumen extracted from the RA and the bituminous binder blend, applying the dynamic shear rheometer (DSR) device. It was shown that for paving grade bitumen (B), polymer modified bitumen (PmB) and rubber modified bitumen (GmB), the addition of higher proportions of RA content is possible without compromising on the performance of the binder blend. With a carefully chosen paving grade bitumen it is possible to utilise up to 40% RA content. For the polymer modified bitumen, the limit of the RA content is 20%. For the rubber modified bitumen, the various proportions of RA contents showed no or negligible changes in the characteristics of the bitumen and the RA content can reach 30% in this case.

The development of autonomous vehicles, both terrestrial and aerial ones (unmanned aerial system (UAS)), is causing the need of properly formulating appropriate solutions for ensuring a safe interaction between them, human beings and the infrastructures and environment in their operating area. To such aim, the knowledge of the positions of different platforms moving in the considered area is fundamental. GNSS (Global Navigation Satellite System) is by far the most used positioning technique in order to determine positions all over the world. Nevertheless, there are several conditions in which its use is unfortunately impossible or unreliable. Hence, different techniques, based on the use of sensors either mounted on the moving platforms or on an ad-hoc infrastructure, shall be used in order to determine the absolute and relative positions of the involved platforms. To this aim, this work proposes the use of vision, in particular from UAS imagery, static LiDAR (Light Detection and Ranging) and UWB (Ultra Wide-Band) transceivers, with initial encouraging results.

Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

The Smart City concept is taking momentum recently as big metropolises as well as mid-size cities are intensifying their efforts to improve the life of people living in dense urban environment. Local governments are eager to have up-to-date information of every aspect of city life, including environmental data, such as air and water quality parameters; mobility data, such as traffic flow, including vehicles, transit passengers; crowd control, such as public events, mobility in hospitals; life quality data, such as social status, education level, health records; etc. Monitoring all these very different data streams in space and time is a formidable challenge. While on the data acquisition side, tremendous progress has been achieved, as sensors have been deployed in increasingly large numbers on both mobile and static platforms, there is a lack of creating accurate geotags, as the quality of georeferencing varies over a large scale. It is important to note that the data acquisition is becoming largely customer-based, as smart devices are efficient sensor systems and with advancing communication technologies, crowdsourcing is quickly becoming the dominant data source on mobile platforms. In this paper, we investigate the potential to exploit the ranging capabilities of imaging and communication sensors and use the strength of the spatial network formed by the sensors to improve the georeferencing of a group of platforms operating in a close environment, such as UAS swarm or a platoon of autonomous vehicles. Transportation in cities and in general mobility are of great interest to Smart Cities, they represent one of the most significant components of the activities, so having an optimized transportation system is essential to reduce carbon footprint, decrease commute time, and just improve the quality of life. To assess the performance of collaborative navigation based accurate georeferencing, data was acquired at a simulated intersection area at The Ohio State University, where multiple vehicles, pedestrians and cyclists were moving around. In addition, drones were flying above the area. Here we report about our initial results.

Reclaimed asphalt pavement (RAP) is generated during road rehabilitation and resurfacing projects. This highly valuable recycled material should be used for manufacturing fresh hot mix asphalt (HMA) for new asphalt pavement layers to ensure the highest added value and minimise environmental impact. The use of RAP is already common practice worldwide; however, incorporating RAP into the manufacturing of HMA is still very minimal in Hungary. As part of a research work HMA containing 20-50% RAP was designed, manufactured and tested. This paper discusses the performance tests carried out on laboratory and plant mixed asphalt mixes; using this data the overall full depth asphalt (FDA) pavement performance was predicted through general mechanistic pavement design. The outcomes of this paper showed that high RAP content asphalt mixes can have superior performance; this disproves the common perception that high RAP mixes are substandard road construction materials. The analysis performed in this paper found that asphalt mixes with high RAP content present low risk for in-situ performance. However, in order to achieve this outcome, the application of correct mix design methodology, appropriate RAP management and suitable asphalt plant capability for mass production are paramount.

NDT related high education is among the highest priorities of the Academia NDT International. Having recognized that NDT competence is increasingly essential to respond to technological development in ensuring long term integrity of structures and of ageing assets, a new profession – NDT integrity engineer and a new university discipline – was introduced. NDT integrity engineering is a profession to develop non-destructive testing and evaluation involving materials science, fracture mechanics, and other sciences that would guarantee and enhance the safety and reliability of engineering structures. Based on the needed competences of NDT integrity engineers the overall basis is the clear understanding of NDT: what can be expected, what are its possibilities and limitations, and how can it serve the structural integrity related decisions in responding to technology driven world, economic, social, and environmental challenges. The paper details the essence and the experiences of the pilot NDT integrity engineering courses completed in Hungary in 2020 and in 2022.

The use of BIM (Building Information Modeling), a component of the Digital Twin concept, is on the rise, and the need for indoor data is rapidly growing. BIM information is not only used for management purposes, but it is essential to support navigation indoors. Observing building interiors by optical sensors, such as cameras and laser scanners, has challenges as the image scale changes over a broad range in rooms and floors, and then complete coverage is required, requiring images taken from several locations with various camera orientations. Using 360° imaging sensors partially addresses the need for efficient wide FOV observations. In this study, we investigate the feasibility of using a 6-sensor omnidirectional/fisheye camera system and report about its performance.

Lattice and gyroid structures are often subjected to additive technologies to produce various types of products, and the current market has a number of 3D printers that can be used for their production. The quality of the products produced in this way can be assessed on the basis of technical parameters and the filament used. Such an approach, however, is insufficient. In terms of quality, other product parameters need to be assessed, such as the surface texture and the internal structure’s porosity. For such an assessment, we can use the industrial tomography method and the method of roughness measurement via an optical microscope. The paper presents research on the assessment of the surface texture and porosity in lattice and gyroid structures. For the research, two types of test specimens—a specimen with a lattice structure and a specimen with a gyroid structure—were prepared. The obtained results proved that the 3D printing technology directly impacted the surface texture and porosity. For experimental specimens produced by SLS technology, we found that it was very important to carefully remove the excess powder, as unremoved powder can significantly affect the porosity results. For specimens produced by FDM technology, the research confirmed that some “gaps” between the layers were not pores but defects created during specimen production. When analyzing the surface using the Alicon Infinite G5 optical microscope, we found that the measured roughness results were directly impacted by the specimen’s surface color, the structure’s geometry, and the ambient light, which was confirmed by a red lattice experimental specimen, the surface of which could not be scanned. Based on the above, it can be stated that the selection of 3D technology for additive production needs must be given adequate attention regarding the quality of the created structures and textures.

Although great progress has been made in the diagnostic and treatment options for dyslipidemias, unawareness, underdiagnosis and undertreatment of these disorders remain a significant global health concern. Growth in digital applications and newer models of care provide novel tools to improve the management of chronic conditions such as dyslipidemia. In this review, we discuss the evolving landscape of lipid management in the 21st century, current treatment gaps and possible solutions through digital health and new models of care. Our discussion begins with the history and development of value-based care and the national establishment of quality metrics for various chronic conditions. These concepts on the level of healthcare policy not only inform reimbursements but also define the standard of care. Next, we consider the advances in atherosclerotic cardiovascular disease risk score calculators as well as evolving imaging modalities. The impact and growth of digital health, ranging from telehealth visits to online platforms and mobile applications, will also be explored. We then evaluate the ways in which machine learning and artificial intelligence-driven algorithms are being utilized to address gaps in lipid management. From an organizational perspective, we trace the redesign of medical practices to incorporate a multidisciplinary team model of care, recognizing that atherosclerotic cardiovascular disease risk is multifaceted and requires a comprehensive approach. Finally, we anticipate the future of dyslipidemia management, assessing the many ways in which atherosclerotic cardiovascular disease burden can be reduced on a population-wide scale.

In this investigation, an attempt was made to develop a new high-strength and high-ductility aluminum metal–matrix composite. It was achieved by incorporating ceramic reinforcement into the metal which was formed in situ from a polymer by pyrolysis. A crosslinked PMHS polymer was introduced into commercially pure aluminum via friction stir processing (FSP). The distributed micro- and nano-sized polymer was then converted into ceramic particles by heating at 500 °C for 10 h and processed again via FSP. The produced composite showed a 2.5-fold increase in yield strength (to 119 MPa from 48 MPa) and 3.5-fold increase in tensile strength (to 286 MPa from 82 MPa) with respect to the base metal. The ductility was marginally reduced from 40% to 30%. The increase in strength is attributed to the grain refinement and the larger ceramic particles. High-temperature grain stability was obtained, with minimal loss to mechanical properties, up to 500 °C due to the Zenner pinning effect of the nano-sized ceramic particles at the grain boundaries. Fractures took place throughout the matrix up to 300 °C. Above 300 °C, the interfacial bonding between the particle and matrix became weak, and fractures took place at the particle–matrix interface.

The agro-ecologic technology proposed, of seed production in red clover, relies on the following technological elements: bacterial inoculation of red clover seeds, application of organic fertilizers or red clover associated cultivation (with a graminaceous plant). Under these conditions, we achieved an average seed yield of 234 kg/ha, 7.3% more than in the classic technology (application of chemical herbicides and fertilizers).

The amount of energy produced (EP) is the energetic equivalent of the forage biomass yield achieved from any type of association. Depending on the fertilization level applied, in the case of the 10 associations studied, the energy amount achieved in a 3-year period was 9181 KWh/ha, respectively 2.7 times bigger than the energy input (EI). The graminaceous and legume associations studied recorded a relatively low average capitalization coefficient of the energy input (0.37), proving the technological efficiency of these crops.

The hydrolysis of the lignocelluloses to fermentable sugars seems to be the main reason for the high producing cost of ethanol from lignocelluloses. The objective of this work is to test two strains of Trichoderma in liquid state and solid state cultures for cellulase production, and to compare the productivity and efficiency of the two systems of fermentation. Submerged liquid cultures (SLC) and solid state cultures (SSC) were carried out to compare the productivity of the two strains of Trichoderma. Comparing the productions of cellulases in the systems applied in this study, data indicate the system of solid state culture with flushing as the most efficient (660% more efficient in T. viride ATCC 13.631 SSC+f than in SLC and 455% more efficient in T. viride CMGB1 SSC+f than in SLC). Still, T. viride CMGB1 show a higher production (2160 FPU in SSC+f) than T. viride ATCC 13.631 (1880 FPU in SSC+fm) in laboratory conditions. These results recommend solid state cultures as systems for producing cellulases at lower price than liquid state cultures. These low cost cellulases can lower the price of ethanol produces from lignocellulosic biomass.

The results obtained reflect the importance to know the way the generative sprigs form, the technological factors that influence the number of sprigs and, finally, the seed production. The utilization at planting of a reduced seed norm (5 kg/ha) strongly influences the sprigging capacity in red fescue, compared with a bigger norm, respectively 20 kg/ha.

Persian clover (Trifolium resupinatum L.) represents an annual forage legume cultivated for forage quality and for its importance as ameliorating plant within the forage crop rotation. The researches performed on the determination of the optimal planting period at the beginning of autumn proved the existence of a direct relationship between the sum of temperatures at planting, at the beginning of winter, and the dry matter yield, respectively between the number of sprigs at the beginning of winter and the content in soluble sugars in roots. According to planting date, the dry matter yield oscillated between 1.5 t/ha in the variant planted on 20th September and 9.4 t/ha in the variant planted on 10th August.

The experimental display included a bifactorial experience, with different proportions of Trifolium resupinatum and Lolium multiflorum-based associations and different Nitrogen fertilization doses (N0, N100). The studies, performed during a 3-year period, made evident the dry matter yield, the total Nitrogen amount and the amount of transferred Nitrogen. According to association’s floristic composition, the amount of N transferred from legume species, on the whole, was between 48 and 145 kg/ha, in the variants without fertilization, and between 36 and 99 kg/ha, in the variants fertilized with N100.

The researches carried out between the altitude levels of 236 and 1300 m, in 13 localities from Banat Mountains, proved the influence exerted by stationary natural conditions on the botanic diversity of the vegetal cover. From one vegetal association to another, the total number of botanic species ranged between 9 and 21; the biggest number of species was recorded between 236 – 630 m altitude, and the smallest one between 462 – 650 m. The mean coverage degree of these species was 42.69% in the species belonging to the Poaceae Family (with a variation of 30-70%) and 12.85% in the species belonging to the Fabaceae Family (variation of 25-65%).

During an experience performed to elaborate an efficient technology of seed production in birdsfoot trefoil, we observed a series of correlations between yield and the elements of productivity, which exert a strong influence on seed quantity. In this viewpoint, the matrix of correlations between the elements of fructification, in the case of the influence exerted by birdsfoot trefoil cultivation method, made evident the following correlation coefficients: r = 0.81**, between the number of pods/plant; r = 0.82**, between the number of generative sprigs and the number of pods/inflorescence; r = 0.98**, between the number of generative sprigs and the number of pods/plant.

The chemical composition of the forage obtained from permanent pastures is determined, in its turn, by the floristic structure consisted of species belonging to various botanic families. Each botanic species presents a specific chemical content and a certain contribution to the balancing of forage’s nutritional value. The chemical analyses performed, at species level, revealed the importance of the “diverse” species, which, with their content in mineral elements, may influence animals’ capacity of production and reproduction. Some of the species, considered to be weeds within the permanent pastures’ floristic composition, presented high crude protein content values: Achillea millefolium with 24.22%, Taraxacum officinale 24.06%, Urtica dioica with 32.46%, Plantago major with 17.04%, etc.

In the cultivation system of the Italian ryegrass (wheat → annual autumn forage legume → Italian ryegrass), compared with the classic cultivation variant in the rotation wheat → Italian ryegrass, where the land remains discovered from wheat harvesting to ryegrass planting, almost 2.5 months, the forage yield increases with 29.6 – 73.0 % according to legume species and yield utilization, by introducing peas or autumn vetch between the wheat and the Italian ryegrass. By introducing legume species within the rotation system, the ryegrass yield increases with 4.94 – 5.11 t/ha DM, in the case of autumn forage peas, and with 3.76 – 5.77 t/ha DM, in the case of autumn vetch, according to the method of utilization of the legume biomass.

Forage quality has a great importance in animal nutrition and is much influenced by the floristic composition structure of the temporary pastures. Legumes play an important role in the production of nutritionally-balanced forage. Analyzing the values of some quality indices of forage (crude protein, crude cellulose, organic matter digestibility, lignin, hemicellulose, ash, ADF, NDF), we may observe a significant increase of these indices in the variants planted in association (Festuca rubra, Trifolium repens, Lotus corniculatus).

Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization and Mapping (VSLAM), etc., where the RANSAC algorithm is frequently used for outlier detection; note that RANSAC is the most widely used robust approach in photogrammetry and computer vision for outlier detection. It is known that the outlier ratio used in RANSAC primarily determines the number of trial runs needed, which eventually, determines the computation time. Over time, different methods have been proposed to reject the false-positive correspondences and improve RANSAC, such as GR_RANSAC, SuperGlue, and LPRANSAC. The specific objective of this study is to propose a filtering algorithm based on Graph Neural Networks (GNN), as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers. The research is based on the idea that descriptors of corresponding points, as well as their spatial relationship, should be similar in image sequences. In graph representation, built by the adjacency matrix of data (nodes features), there should be similarity for corresponding points that are close to each other in the image domain. From the many GNNs techniques, Graph Attention Networks (GATs) were selected for this study as they assign different importance to each neighbour’s contribution as anisotropic operations, so the features of neighbour nodes are not considered in the same way, unlike other GNNs techniques. In our approach, we build a graph in each image, because the similarity of the two-dimensional spatial relationships between points in the image domain of consecutive images should be similar. Then during processing, points with any significantly different neighbours are considered as outliers. Next, the points can be updated in the GNN layer. GNN-RANSAC is tested experimentally on real image pairs. Clearly, the proposed pre-filtering increases the inlier ratio and results in faster convergence compared to ordinary RANSAC, making it attractive for real-time applications. Furthermore, there is no need to learn the features.

Mapping platforms jointly operating in a formation are increasingly used to improve the efficiency of geospatial data acquisition recently. For example, UAS swarm mapping is gaining market share or robot platform are used for indoor mapping. These platforms are typically equipped with imaging and navigation sensors as well as have various communication capabilities. Until now, the platforms are individually navigated and georeferenced. Since the platforms are typically sharing a small area, and thus, they are within their sensing range; for example, they can see and thus track each other in optical or lidar imagery. Furthermore, new communication technologies have started to provide ranging information between communication points. Using the ranges between platforms makes it feasible to create a local geodetic network defined by the platforms. The geometric strength of the network then can be exploited to support platform georeferencing. In this study, the network formation based on ranges is investigated. Initial experiences are reported on the impact of the size of the network, the spatial distribution of the network nodes and the number of available ranges.

Indoor mapping is gaining more interest in both research as well as in emerging applications. Building information systems (BIM) and indoor navigation are probably the driving force behind this trend. For accurate mapping, the platform trajectory reconstruction, or in other words sensor orientation, is essential to reduce or even eliminate for extensive ground control. Simultaneous localization and mapping (SLAM) is the computation problem of how to simultaneously estimate the platform/sensor trajectory while reconstructing the object space; usually, a real-time operation is assumed. Here we investigate the performance of two LiDAR SLAM tools based on using indoor data, acquired by a remotely controlled robot sensor platform. All comparisons were performed on similar datasets using appropriate metrics and encouraging results were obtained as a consequence of initial test studies yet further research is needed to analyse these tools and their accuracy comprehensively.

The increasing interest in autonomous vehicles motivates the researches aiming at developing reliable positioning system also in conditions challenging for the Global Navigation Satellite Systems (GNSS), such as in urban canyons, tunnels, under quite dense vegetation. The uso of Ultra Wide-Band (UWB) systems is among the quite well known methods for providing reasonable positioning results without exploiting GNSS. UWB systems are typically used indoors, however their use can be of interest also outdoors, in particular when the need is to ensure good positioning results over a quite small area. This paper investigates the use of UWB systems for positioning in the case of terrestrial vehicles, and, more specifically, it focuses on checking the influence of car platoon configurations on the performance of an UWB cooperative positioning system. In the considered tests, where a high percentage of UWB communications was successful, the obtained results show that the car configuration can have a quite remarkable impact on the positioning performance, doubling the obtained median error.

Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.

One of the most efficient measures that contributes to forage improvement is represented by the temporary pastures consisted of legumes and perennial gramineae. Beside Agrostis tenuis, Festuca rubra is the forage gramineae with the largest spread and dominance in our country. That is why its introduction in various associations with forage legumes leads to qualitative and quantitative improvement of the yields obtained on temporary pastures.

This work presents the potential and real seed producing capacity in some Romanian varieties of legumes and perennial gramineae: Trifolium repens, Lotus corniculatus, Lolium perenne, Festuca pratensis, Festuca arundinacea and Dactylis glomerata. To calculate the potential production, we performed determinations and analyses on each variety, regarding floral apparatus’ morphological and anatomic structure (number of inflorescences, number of flowers, number of ovules/ovary), and the real production was determined „in situ”.

This work studies the yielding and grazing capacity of some permanent pastures located in Banat’s Mountains, at 236 – 1300 m altitude. The mean results achieved showed that the yield difference between the minimal altitude level (236 m) and the maximal one (1300 m) is 0.93 t/ha DM. This difference leads to the conclusion that the yield of the permanent pastures located in the studied area decreases with 0.87 kg/ha DM (about 0.5 t/ha fresh mass) for each 100 m of altitude.

Egyptian clover (Trifolium alexandrinum L.) represents an annual legume species with importance in animal nutrition. Researches proved the possibility of introducing this species in the floristic structure of the temporary pastures planted in spring, in order to increase dry matter and to make crops efficient from the first year of vegetation. The results achieved showed that in the variant planted with 22 kg/ha alfalfa + 4 kg/ha orchard grass + 6 kg/ha Egyptian clover, the forage yield increased with 20% compared with the same variant without Egyptian clover.