Search

Mixed Reality (MR) Head Mounted Displays (HMDs) offer a hitherto underutilized set of advantages compared to conventional 3D scanners. These benefits, inherent to MR-HMDs albeit not originally intended for such applications, encompass the freedom of hand movement, hand tracking capabilities, and real-time mesh visualization. This study leverages these attributes to enhance indoor scanning process. The primary innovation lies in the conceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach. The proposed methodology is effectively implemented using the HoloLens 2 platform. An application is designed to enable the remote placement of virtual tags based on the user's visual focus on the MR-HMD display. This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities, as well as augmented tag visualization through overlaying them on real-world objects. To assess the practicality of the proposed method, a comprehensive real-world case study spanning an area of 330 s2 is conducted. Remarkably, the survey demonstrates remarkable efficiency, with 20 virtual tags swiftly deployed, each requiring a mere 2 s for precise positioning. Subsequently, these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation. The accuracy of virtual tag positioning is found to be exceptional, with an average error of 2.4 ± 1.8 cm. Importantly, the user experience is significantly enhanced, leading to improved seed positioning and, consequently, more accurate final segmentation results.

Clinical prediction of nontuberculous mycobacteria lung disease (NTM-LD) progression remains challenging. We aimed to evaluate antigen-specific immunoprofiling utilizing flow cytometry (FC) of activation-induced markers (AIM) and IFN-γ enzyme-linked immune absorbent spot assay (ELISpot) accurately identifies patients with NTM-LD, and differentiate those with progressive from nonprogressive NTM-LD. A Prospective, single-center, and laboratory technician-blinded pilot study was conducted to evaluate the FC and ELISpot based immunoprofiling in patients with NTM-LD (n = 18) and controls (n = 22). Among 18 NTM-LD patients, 10 NTM-LD patients were classified into nonprogressive, and 8 as progressive NTM-LD based on clinical and radiological features. Peripheral blood mononuclear cells were collected from patients with NTM-LD and control subjects with negative QuantiFERON results. After stimulation with purified protein derivative (PPD), mycobacteria-specific peptide pools (MTB300, RD1-peptides), and control antigens, we performed IFN-γ ELISpot and FC AIM assays to access their diagnostic accuracies by receiver operating curve (ROC) analysis across study groups. Patients with NTM-LD had significantly higher percentage of CD4+/CD8+ T-cells co-expressing CD25+CD134+ in response to PPD stimulation, differentiating between NTM-LD and controls. Among patients with NTM-LD, there was a significant difference in CD25+CD134+ co-expression in MTB300-stimulated CD8+ T-cells (p

In the realm of transportation system management, various remote sensing techniques have proven instrumental in enhancing safety, mobility, and overall resilience. Among these techniques, Light Detection and Ranging (LiDAR) has emerged as a prevalent method for object detection, facilitating the comprehensive monitoring of environmental and infrastructure assets in transportation environments. Currently, the application of Artificial Intelligence (AI)-based methods, particularly in the domain of semantic segmentation of 3D LiDAR point clouds by Deep Learning (DL) models, is a powerful method for supporting the management of both infrastructure and vegetation in road environments. In this context, there is a lack of open labeled datasets that are suitable for training Deep Neural Networks (DNNs) in transportation scenarios, so, to fill this gap, we introduce ROADSENSE (Road and Scenic Environment Simulation), an open-access 3D scene simulator that generates synthetic datasets with labeled point clouds. We assess its functionality by adapting and training a state-of-the-art DL-based semantic classifier, PointNet++, with synthetic data generated by both ROADSENSE and the well-known HELIOS++ (HEildelberg LiDAR Operations Simulator). To evaluate the resulting trained models, we apply both DNNs on real point clouds and demonstrate their effectiveness in both roadway and forest environments. While the differences are minor, the best mean intersection over union (MIoU) values for highway and national roads are over 77%, which are obtained with the DNN trained on HELIOS++ point clouds, and the best classification performance in forested areas is over 92%, which is obtained with the model trained on ROADSENSE point clouds. This work contributes information on a valuable tool for advancing DL applications in transportation scenarios, offering insights and solutions for improved road and roadside management.

The field of pharmacogenetics (PGx) holds great promise in advancing personalized medicine by adapting treatments based on individual genetic profiles. Despite its benefits, there are still economic, ethical and institutional barriers that hinder its implementation in our healthcare environment. A retrospective analysis approach of anonymized data sourced from electronic health records was performed, encompassing a diverse patient population and evaluating key parameters such as prescribing patterns and test results, to assess the impact of pharmacogenetic testing. A head-to-head comparison with previously published activity results within the same pharmacogenetic laboratory was also conducted to contrast the progress made after 10 years. The analysis revealed significant utilization of pharmacogenetic testing in daily clinical practice, with 1,145 pharmacogenetic tests performed over a 1-year period and showing a 35% growth rate increase over time. Of the 17 different medical departments that sought PGx tests, the Oncology department accounted for the highest number, representing 58.47% of all genotyped patients. A total of 1,000 PGx tests were requested for individuals susceptible to receive a dose modification based on genotype, and 76 individuals received a genotype-guided dose adjustment. This study presents a comprehensive descriptive analysis of real-world data obtained from a public tertiary hospital laboratory specialized in pharmacogenetic testing, and presents data that strongly endorse the integration of pharmacogenetic testing into everyday clinical practice.

Abstract Introduction Transport infrastructures have an important function in society and the development of a country. In Spain, the most used modes of traveler transport are road and rail, far ahead of other means of transport such as air or maritime transport. Both rail and road infrastructures can be affected by numerous hazards, endangering their performance and the safety of users. This study proposes a methodology with a multiscale top-down approach to identify the areas affected by fire, landslide, and safety in road and rail infrastructures in Galicia (Northwest Spain). Methodology The methodology is developed in three steps, coinciding with the three scales considered in this work: network-, system-, and object-level. In the first step, risk areas are identified and prioritized, resulting in the most critical safety risk in a motorway section. This area defines a study scenario composed of a location (A-55 motorway) and the associated risk (road safety). In the second step, the road safety factors within this scenario are selected, hierarchized, and weighted using a combination of Multi-Criteria Decision-Making methods including the Analytical Hierarchy Process and the Best–Worst Method. Finally, a risk map is generated based on the weighting of infrastructure-related safety factors and compared to real historical accident data for validation. The methodology is based on road and risk assessment standards and only information in the public domain is used. Results Results show that only 3 segments out of 153 were classified incorrectly, which supports a probability higher than 95% of agreement with real data (at 5% significance level). In a conclusion, the overall methodology exhibits a high potential for hazard prevention and road-safety enhancement.

Introduction There is a need to optimise the management of atopic dermatitis (AD), improving the efficacy of treatments and reducing the toxicity associated with them. Although the efficacy of ciclosporine (CsA) in the treatment of AD has been thoroughly documented in the literature, the optimal dose has not been yet established. The use of multiomic predictive models of treatment response could optimise CsA therapy in AD.Methods and analysis The study is a low-intervention phase 4 trial to optimise the treatment of patients with moderate-severe AD requiring systemic treatment. The primary objectives are to identify biomarkers that could allow for the selection of responders and non-responders to first-line treatment with CsA and to develop a response prediction model to optimise the CsA dose and treatment regimen in responding patients based on these biomarkers. The study is divided into two cohorts: the first comprised of patients starting treatment with CsA (cohort 1), and the second, of patients already receiving or who have received CsA therapy (cohort 2).Ethics and dissemination The study activities began following authorisation by the Spanish Regulatory Agency (AEMPS) and the Clinical Research Ethics Committee of La Paz University Hospital approval. Trial results will be submitted for publication in an open access peer-reviewed medical speciality-specific publication.Trial registration of this study can be located at the EU Clinical Trials Register, available from https://euclinicaltrials.eu/search-for-clinical-trials/?lang=en. Our clinical trial was registered in the website before the enrolment of the first patient complying with European regulations. EU Clinical Trials Register is a primary registry according the WHO. Once our trial was included in a primary and official registry, in order to extend the accessibility to our research, we also registered it retrospectively in clinicaltrials.gov; however, this is not mandatory as per our regulation.Trial registration number NCT05692843.

The pavement distress measurement is a crucial aspect in guaranteeing the safety of transportation infrastructure. In this regard, we introduce a novel and cost-effective multi-sensor approach for pavement segmentation during low-light night conditions. By utilizing the low-cost Azure Kinect multi-sensor system, we generated a multi-sensor dataset that encompasses aligned IR, RGB, and depth images. Then we carried out the data annotation process on the RGB images. A total of 11,343 manual annotations were meticulously made on 791 images, which were randomly selected from a collection of 96,891 frames. Subsequently, four different deep learning-based image segmentation models were analyzed both quantitatively and qualitatively. The results indicated that the segmentation performance on the IR dataset outperformed that of the RGB dataset. The model with the highest mIoU (mean Intersection over Union) of 0.7169 was ConvNext when trained on the IR dataset. Furthermore, we proposed the use of relative height for evaluating the severity of pavement distress. On the aligned depth map, the relative height was calculated using the depth data from the corresponding pavement distress area. Additionally, a quantitative comparison between manual annotations and the results obtained through deep learning revealed that the latter was more effective in identifying more severe forms of pavement distress. Through this study, we established the feasibility of collecting pavement distress data during nighttime using a low-cost multi-sensor system.

This paper presents a model of a cone using reinforcement learning, harnessing the self-learning capacity of Artificial Intelligence to improve process efficiency. The independent operation of the cone is achieved through a reward and punishment system based on approaching or reaching the goal. The cone must decide between 0° or 90° turns at each step to maximize long-term rewards. While the simulated robotic safety cones successfully reach their targets, the training process is time-consuming due to the numerous variables involved. Nonetheless, the rise of AI and its self-learning capabilities offer promising opportunities for process optimization.

Abstract Background It remains elusive how the characteristics, the course of disease, the clinical management and the outcomes of critically ill COVID-19 patients admitted to intensive care units (ICU) worldwide have changed over the course of the pandemic. Methods Prospective, observational registry constituted by 90 ICUs across 22 countries worldwide including patients with a laboratory-confirmed, critical presentation of COVID-19 requiring advanced organ support. Hierarchical, generalized linear mixed-effect models accounting for hospital and country variability were employed to analyse the continuous evolution of the studied variables over the pandemic. Results Four thousand forty-one patients were included from March 2020 to September 2021. Over this period, the age of the admitted patients (62 [95% CI 60–63] years vs 64 [62–66] years, p

The transport infrastructure of a country facilitates the development and growth of its economy and improves the quality of life of its inhabitants. Increasing its resilience to different types of risks to improve performance is becoming more important. In the current context of climate change, natural hazards are more severe and frequent. In this article, we focus on rockfall as a natural hazard for roads that occurs in small areas in the vicinity of natural or cut slopes, causing road safety problems by invading part of the road.This article aims to inventory the slopes along the road, identifying the area of the road which would be invaded in case of a rockfall. A methodology divided into two blocks is proposed. First, for slope detection and inventory, an algorithm is developed based on open LiDAR point clouds analysis. The second block consists of estimating the invaded road area if a rockfall occurs on each of the inventoried slopes, using a combination of RockGIS software and the Monte Carlo method. The methodology was applied in five case studies: three sections on motorways and two sections on national roads. The results obtained for slope detection show higher rates in the case studies analyzing motorways, with a precision of 100%, a recovery rate of greater than 93.4%, and an F1 score of greater than 0.96. The results in the invaded area of the road show that 11 slopes would cause a total cut of the motorway in one of the directions if a rockfall occurs.These results are useful for infrastructure managers to remotely obtain an inventory of road slopes and know which of them would affect road safety. Also, the results can serve as input for the Intelligent Transportation System and allow the exchange of information under the Building Information Model approach.

The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner.

Our working hypothesis was that magnesium (Mg) supplementation modulates plant performance under low water availability and improves drought tolerance in soybean genotypes. Plants of Bônus 8579, M8808 and TMG1180 genotypes were grown under field conditions and subjected to three water stress treatments (control, moderate and severe stress) and three Mg levels [0.9 (low), 1.3 (adequate) and 1.7 cmolc dm-³ (supplementation)]. After 28 days of drought imposition, the growth parameters, osmotic potential, relative water content, leaf succulence, Mg content and photosynthetic pigments were assessed. In general, drought drastically decreased the growth in all genotypes, and the reductions were intensified from moderate to severe stress. Under adequate Mg supply, TMG1180 was the most drought-tolerant genotype among the soybean plants, but Mg supplementation did not improve its tolerance. Conversely, although the M8808 genotype displayed inexpressive responses to drought under adequate Mg, the Mg-supplemented plants were found to have surprisingly better growth performance under stress compared to Bônus 8579 and TMG1180, irrespective of drought regime. The improved growth of high Mg-treated M8808-stressed plants correlated with low osmotic potential and increased relative water content, as well as shoot Mg accumulation, resulting in increased photosynthetic pigments and culminating in the highest drought tolerance. The results clearly indicate that Mg supplementation is a potential tool for alleviating water stress in M8808 soybean plants. Our findings suggest that the enhanced Mg-induced plant acclimation resulted from increased water content in plant tissues and strategic regulation of Mg content and photosynthetic pigments.

The mobile mapping system (MMS) could become the foundation of digital twins and 3D modeling, and is widely applicable in a variety of fields, such as infrastructure management, intelligent transportation systems, and smart cities. However, data collected by MMS is extensive and complex, making data processing difficult. We present a novel method for segmenting urban assets (specifically in this case study traffic signs) with a lower-cost Azure Kinect and automatic data processing workflows. First, it was necessary to verify the reliability of this approach using the Time of Flight (ToF) camera from Azure Kinect to detect road signs outdoors. Using the data generated by the ToF camera, we then extracted the Region of Interest (ROI) quickly and efficiently. After transforming the ROI to the RGB image, we obtained the traffic sign area through a hybrid color-shape based method. In addition, we calculated the distance between the traffic sign and Azure Kinect based on the depth image. The Coefficient of Variation cv averaged 1.1%. It is thus evident that Azure Kinect is reliable for outdoor traffic sign segmentation. Our algorithm has been compared with deep learning algorithms. According to our analysis, our algorithm has an accuracy of 0.8216, while the accuracy of deep learning is 0.7466, which indicates that our solution is more flexible and cost-effective.

Platin-based chemotherapy is the standard treatment for patients with non-small cell lung cancer (NSCLC). However, resistance to this therapy is a major obstacle in successful treatment. In this study, we aimed to investigate the impact of several pharmacogenetic variants in patients with unresectable NSCLC treated with platin-based chemotherapy. Our results showed that DPYD variant carriers had significantly shorter progression-free survival and overall survival compared to DPYD wild-type patients, whereas DPD deficiency was not associated with a higher incidence of high-grade toxicity. For the first time, our study provides evidence that DPYD gene variants are associated with resistance to platin-based chemotherapy in NSCLC patients. Although further studies are needed to confirm these findings and explore the underlying mechanisms of this association, our results suggest that genetic testing of DPYD variants may be useful for identifying patients at a higher risk of platin-based chemotherapy resistance and might be helpful in guiding future personalized treatment strategies in NSCLC patients.

The triple-negative breast cancer (TNBC) subtype comprises approximately 15% of all breast cancers and is associated with poor long-term outcomes. Classical chemotherapy remains the standard of treatment, with toxicity and resistance being major limitations. TNBC is a high metabolic group, and antimetabolic drugs are effective in inhibiting TNBC cell growth. We analyzed the combined effect of chemotherapy and antimetabolic drug combinations in MDA-MB-231, MDA-MB-468 and HCC1143 human TNBC cell lines. Cells were treated with each drug or with drug combinations at a range of concentrations to establish the half-maximal inhibitory concentrations (IC50). The dose-effects of each drug or drug combination were calculated, and the synergistic or antagonistic effects of drug combinations were defined. Chemotherapy and antimetabolic drugs exhibited growth inhibitory effects on TNBC cell lines. Antimetabolic drugs targeting the glycolysis pathway had a synergistic effect with chemotherapy drugs, and antiglycolysis drug combinations also had a synergistic effect. The use of these drug combinations could lead to new therapeutic strategies that reduce chemotherapy drug doses, decreasing their toxic effect, or that maintain the doses but enhance their efficacy by their synergistic effect with other drugs.

The automatic definition of the ground from 3D point clouds has been a common process for the last two decades, with many different approaches and applications that can be found in a vast literature. This paper presents a comparison of three different methodological concepts for ground classification, in order to establish the advantages and drawbacks of each method. First, a heuristic method, based on previous knowledge of the geometry and context of the 3D data. Secondly, a Deep Convolutional Network based on SegNet that classifies 2D images generated from the 3D point cloud. Finally, the third method applies a Deep Learning classification based on PointNet, which takes 3D points directly as inputs. To validate each method and compare them, public and labelled point clouds from the Actueel Hoogtebestand Nederland dataset are employed. Furthermore, the three methods are validated against the ISPRS 3D Semantic Labeling Contest benchmark. The results obtained show that the deep learning-based approaches outperform the heuristic method, with F-scores above 96%. The best results were obtained using a shallower version of SegNet, with F-score above 97%.

Unmanned Aerial Vehicles (UAVs) are part of our daily lives with a number of applications in diverse fields. On many occasions, developing these applications can be an arduous or even impossible task for users with a limited knowledge of aerial robotics. This work seeks to provide a middleware programming infrastructure that facilitates this type of process. The presented infrastructure, named DroneWrapper, offers the user the possibility of developing applications abstracting the user from the complexities associated with the aircraft through a simple user programming interface. DroneWrapper is built upon the de facto standard in robot programming, Robot Operating System (ROS), and it has been implemented in Python, following a modular design that facilitates the coupling of various drivers and allows the extension of the functionalities. Along with the infrastructure, several drivers have been developed for different aerial platforms, real and simulated. Two applications have been developed in order to exemplify the use of the infrastructure created: follow-color and follow-person. Both applications use techniques of computer vision, classic (image filtering) or modern (deep learning), to follow a specific-colored object or to follow a person. These two applications have been tested on different aerial platforms, including real and simulated, to validate the scope of the offered solution.

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.

Periods of soil water stress have been recurrent in the Cerrado region and have become a growing concern for Brazilian tropical pasture areas. Thus, the search for forage grasses more tolerant to water stress has intensified recently in order to promote more sustainable livestock. In a greenhouse experiment, the degree of water stress tolerance of nine tropical forage grass cultivars was studied under different soil water regimes. The investigation followed a 9 × 3 factorial design in four randomized blocks. Nine cultivars from five species of perennial forage grasses were tested: Urochloa brizantha (‘BRS Piatã’, ‘Marandu’, and ‘Xaraés’), Panicum maximum (‘Aruana’, ‘Mombaça’, and ‘Tanzânia’), Pennisetum glaucum (‘ADR 300’), Urochloa ruziziensis (‘Comum’), and Paspalum atratum (‘Pojuca’). These cultivars were grown in pots under three soil water regimes (high soil water regime—HSW (non-stressful condition), middle soil water regime—MSW (moderate water stress), and low soil water regime—LSW (severe water stress)). Plants were exposed to soil water stress for 25 days during the tillering and stalk elongation phases. Twelve tolerance indices, including tolerance index (TOL), mean production (MP), yield stability index (YSI), drought resistance index (DI), stress tolerance index (STI), geometric mean production (GMP), yield index (YI), modified stress tolerance (k1STI and k2STI), stress susceptibility percentage index (SSPI), abiotic tolerance index (ATI), and harmonic mean (HM), were calculated based on shoot biomass production under non-stressful (YP) and stressful (YS) conditions. Soil water stress decreased leaf area, plant height, tillering capacity, root volume, and shoot and root dry matter production in most cultivars, with varying degrees of reduction among tropical forage grasses. Based on shoot biomass production under controlled greenhouse conditions, the most water-stress-tolerant cultivars were P. maximum cv. Mombaça and cv. Tanzânia under the MSW regime and P. maximum cv. Aruana and cv. Mombaça under the LSW regime. P. maximum cv. Mombaça has greater adaptability and stability of shoot biomass production when grown under greenhouse conditions and subjected to soil water stress. Therefore, this forage grass should be tested under field conditions to confirm its forage production potential for cultivation in tropical regions with the occurrence of water stress. The MP, DI, STI, GMP, YI, k2STI, and HM tolerance indices were the most suitable for identifying forage grass cultivars with greater water stress tolerance and a high potential for shoot biomass production under LSW regime.

Analysis of emission allowances prices has important environmental and political connotations. This article aimed to identifying the possible variables that may influence their behaviour and studied their relationship with fundamental factors: energy (Brent petroleum, Gas, Coal) and economy (Industrial Production Index, Baltic Dry Index, Purchasing Managers Index). With the objective of analyzing possible mutual interactions, Multivariate VAR or Error Correction Models (VECM), were applied. The information analysed derived from different sources (World Bank, Sendeco2 and various financial websites). The results obtained showed, not only the influence of past prices on the emission allowances actual price, but also the interaction with energetic and economic variables. Highlights Estimation of time series interrelations through VAR models. There is relationship of the emission allowances price with their past values. The energy variables are factors important to also explain the behavior of the emission allowances price. The economic variables are hardly significant except for the Dry Baltic Index.

Background: Pulmonary arterial hypertension (PAH) is a rare disease characterized by pulmonary vascular remodeling and right heart failure. Specific genetic variants increase the incidence of PAH in carriers with a family history of PAH, those who suffer from certain medical conditions, and even those with no apparent risk factors. Inflammation and immune dysregulation are related to vascular remodeling in PAH, but whether genetic susceptibility modifies the PAH immune response is unclear. TNIP2 and TRAF2 encode for immunomodulatory proteins that regulate NF-κB activation, a transcription factor complex associated with inflammation and vascular remodeling in PAH.Methods: Two unrelated families with PAH cases underwent whole-exome sequencing (WES). A custom pipeline for variant prioritization was carried out to obtain candidate variants. To determine the impact of TNIP2 and TRAF2 in cell proliferation, we performed an MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assay on healthy lung pericytes transfected with siRNA specific for each gene. To measure the effect of loss of TNIP2 and TRAF2 on NF-kappa-beta (NF-κB) activity, we measured levels of Phospho-p65-NF-κB in siRNA-transfected pericytes using western immunoblotting.Results: We discovered a novel missense variant in the TNIP2 gene in two affected individuals from the same family. The two patients had a complex form of PAH with interatrial communication and scleroderma. In the second family, WES of the proband with PAH and primary biliary cirrhosis revealed a de novo protein-truncating variant in the TRAF2. The knockdown of TNIP2 and TRAF2 increased NF-κB activity in healthy lung pericytes, which correlated with a significant increase in proliferation over 24 h.Conclusions: We have identified two rare novel variants in TNIP2 and TRAF2 using WES. We speculate that loss of function in these genes promotes pulmonary vascular remodeling by allowing overactivation of the NF-κB signaling activity. Our findings support a role for WES in helping identify novel genetic variants associated with dysfunctional immune response in PAH.

Building information modelling (BIM) is evolving significantly in the architecture, engineering and construction industries. BIM involves various remote-sensing tools, procedures and standards that are useful for collating the semantic information required to produce 3D models. This is thanks to LiDAR technology, which has become one of the key elements in BIM, useful to capture a semantically rich geometric representation of 3D models in terms of 3D point clouds. This review paper explains the ‘Scan to BIM’ methodology in detail. The paper starts by summarising the 3D point clouds of LiDAR and photogrammetry. LiDAR systems based on different platforms, such as mobile, terrestrial, spaceborne and airborne, are outlined and compared. In addition, the importance of integrating multisource data is briefly discussed. Various methodologies involved in point-cloud processing such as sampling, registration and semantic segmentation are explained in detail. Furthermore, different open BIM standards are summarised and compared. Finally, current limitations and future directions are highlighted to provide useful solutions for efficient BIM models.

Pulmonary arterial hypertension (PAH) sometimes co-exists with hereditary hemorrhagic telangiectasia (HHT). Despite being clinically diagnosable according to Curaçao criteria, HHT can be difficult to diagnose due to its clinically heterogenicity and highly overlapping with PAH. Genetic analysis of the associated genes ACVRL1, ENG, SMAD4 and GDF2 can help to confirm or discard the presumptive diagnosis. As part of the clinical routine and to establish a genetic diagnosis, we have analyzed a cohort of patients with PAH and overlapping HHT features through a customized Next Generation Sequencing (NGS) panel of 21 genes, designed and validated in-house. We detected a homozygous missense variant in GDF2 in a pediatric patient diagnosed with PAH associated with HHT and a missense variant along with a heterozygous deletion in another idiopathic PAH patient (compound heterozygous inheritance). In order to establish variant segregation, we analyzed all available family members. In both cases, parents were carriers for the variants, but neither was affected. Our results expand the clinical spectrum and the inheritance pattern associated with GDF2 pathogenic variants suggesting incomplete penetrance and/or variability of expressivity with a semi-dominant pattern of inheritance.

Individual tree (IT) segmentation is crucial for forest management, supporting forest inventory, biomass monitoring or tree competition analysis. Light detection and ranging (LiDAR) is a prominent technology in this context, outperforming competing technologies. Aerial laser scanning (ALS) is frequently used for forest documentation, showing good point densities at the tree-top surface. Even though under-canopy data collection is possible with multi-echo ALS, the number of points for regions near the ground in leafy forests drops drastically, and, as a result, terrestrial laser scanners (TLS) may be required to obtain reliable information about tree trunks or under-growth features. In this work, an IT extraction method for terrestrial backpack LiDAR data is presented. The method is based on DBSCAN clustering and cylinder voxelization of the volume, showing a high detection rate (∼90%) for tree locations obtained from point clouds, and low commission and submission errors (accuracy over 93%). The method includes a sensibility assessment to calculate the optimal input parameters and adapt the workflow to real-world data. This approach shows that forest management can benefit from IT segmentation, using a handheld TLS to improve data collection productivity.

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

Public administrations have in recent years developed programs of public funding for innovation to boost the competitiveness of business. The study of how companies have used these funding sources generates knowledge to improve the design of support for private innovation and to provide advice for innovative companies. This paper investigates these issues in the agri-food sector which is of particular interest as it is comprised mainly of small and medium enterprises with a wide regional presence and interaction with their local environment. A survey on technological innovation was used to estimate panel logit models with random effects, taking as dependent variables three types of funding: regional, state and European Union. The results generally show a positive relationship between innovation efforts and access to public funding, but also significant differences between types of funding and between sectors. Food companies that obtain public funding tend to have a more innovative profile than Agriculture ones. Both types of firm present higher probabilities than others companies when it comes to gaining access to regional funding, though the opposite often occurs in the case of state funding. Firm size is not significant for regional funding and no overlap was detected between regional and state funding. The financial crisis has adversely affected regional and national aid, which experienced a significant decrease in the period from 2008 to 2013.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

This paper proposes an efficient and simplified procedure for the 3D modelling of buildings, based on the semi-automatic processing of point clouds acquired with mobile LiDAR scanners. The procedure is designed with the aim at generating BIM, in gbXML format, from the point clouds. In this way, the main application of the procedure is the performance of energy analysis, towards the increase of the energy efficiency in the construction sector, and its consequent contribution to the mitigation of the climate change. Thus, the main contribution of the methodology proposed is its easiness of use and its level of automation, which allow its utilization by users who are experts in the use of energy in buildings but non-experts on 3D modelling. The software provides a solution for the 3D modelling of complex point clouds of various millions of points in times of execution less than 10 minutes. The system is evaluated through its application to three different real-world scenarios and compared with manual modelling. Moreover, the results are used for an example of an energy application, proving their performance against manually elaborated models.

The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches.

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection−classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed.

3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

Improving the resilience of infrastructures is key to reduce their risk vulnerability and mitigate impact from hazards at different levels (e.g., from increasing extreme events, driven by climate change); or from human-made events such as: accidents, vandalism or terrorist actions. One of the most relevant aspects of resilience is preparation. This is directly related to: (i) the risk prediction capability; (ii) the infrastructure monitoring; and (iii) the systems contributing to anticipate, prevent and prepare the infrastructure for potential damage. This work focuses on those methods and technologies that contribute to more efficient and automated infrastructure monitoring. Therefore, a review that summarizes the state of the art of LiDAR (Light Detection And Ranging)-based data processing is presented, giving a special emphasis to road and railway infrastructure. The most relevant applications related to monitoring and inventory transport infrastructures are discussed. Furthermore, different commercial LiDAR-based terrestrial systems are described and compared to offer a broad scope of the available sensors and tools to remote monitoring infrastructures based on terrestrial systems.

In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost.

Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a way to automatically estimate the required excavation volume when widening a road from point cloud data acquired by an LMMS. Firstly, the input point cloud is down-sampled to a uniform grid and outliers are removed. For each of the resulting grid points, both on and off the road, the local surface normal and 2D slope are estimated. Normals and slopes are consecutively used to separate road from off-road points which enables the estimation of the road centerline and road boundaries. In the final step, the left and right side of the road points are sliced in 1-m slices up to a distance of 4 m, perpendicular to the roadside. Determining and summing each sliced volume enables the estimation of the required excavation for a widening of the road on the left or on the right side. The procedure, including a quality analysis, is demonstrated on a stretch of a mountain road that is approximately 132 m long as sampled by a Lynx LMMS. The results in this particular case show that the required excavation volume on the left side is 8% more than that on the right side. In addition, the error in the results is assessed in two ways. First, by adding up estimated local errors, and second, by comparing results from two different datasets sampling the same piece of road both acquired by the Lynx LMMS. Results of both approaches indicate that the error in the estimated volume is below 4%. The proposed method is relatively easy to implement and runs smoothly on a desktop PC. The whole workflow of the LMMS data acquisition and subsequent volume computation can be completed in one or two days and provides road engineers with much more detail than traditional single-point surveying methods such as Total Station or GPS profiling. A drawback is that an LMMS system can only sample what is within the view of the system from the road.

Durante los últimos años, las técnicas geomáticas se han aplicado de forma creciente a muchos aspectos relacionados con la ingeniería de la construcción. La aplicación de estas técnicas en este ámbito viene motivado por la necesidad de usar geometría real y de precisión in varias fases del proceso constructivo, así como por las ventajas derivadas del empleo de técnicas no destructivas, que permiten, entre otros, reducir riesgos durante el proceso de medición de estructuras construídas, reducir costes durante los trabajos de adquisición de datos en campo, y principalmente, reducir costes a través de la automatización de los procesos. El grupo de investigación TF1- Fotogrametría y Teledetección Cercanas- es un equipo de investigadores de la Universidad de Vigo (España) que ha desarrollado y validado varias técnicas de medición geomática en el ámbito terrestre a diferentes campos relacionados con la ingeniería de construcción. Después de aplicar estas técnicas durante una década, se ha demostrado que dichas técnicas han beneficiado de forma significativa los procedimientos de documentación, trabajos de inspección, y además, han resuelto con éxito algunas de las principales limitaciones en cuanto al registro de ciertos parámetros esenciales en la evaluación de la estabilidad de las estructuras. Este artículo presenta una compilación de trabajos donde la fotogrametría y el laser scanner terrestres, georradar y la termografía infrarroja son aplicados a situaciones o actividades relacionados con procesos contructivos.

This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics.

Health monitoring and prediction in different types of structures is essential in order to maintain optimal conditions. Some of the pathologies that affect their structural stability are characterized by distinct thermal properties compared to unaltered areas. Infrared thermography (IRT) is a technique based on the acquisition of the thermal radiation of the bodies using thermal sensors of infrared (IR) cameras, which produce an image of the thermal infrared radiation captured through the conversion of the radiation values to temperature values. Therefore, this technique can be used in different studies to analyse structures with one or more pathologies based on their anomalous thermal behaviour with regard to the unaltered surroundings. As a consequence, this review presents various IRT applications to infrastructure inspections, showing the utility of the technique.

This paper presents a Wearable Prototype for indoor mapping developed by the University of Vigo. The system is based on a Velodyne LiDAR, acquiring points with 16 rays for a simplistic or low-density 3D representation of reality. With this, a Simultaneous Localization and Mapping (3D-SLAM) method is developed for the mapping and generation of 3D point clouds of scenarios deprived from GNSS signal. The quality of the system presented is validated through the comparison with a commercial indoor mapping system, Zeb-Revo, from the company GeoSLAM and with a terrestrial LiDAR, Faro Focus3D X330. The first is considered as a relative reference with other mobile systems and is chosen due to its use of the same principle for mapping: SLAM techniques based on Robot Operating System (ROS), while the second is taken as ground-truth for the determination of the final accuracy of the system regarding reality. Results show that the accuracy of the system is mainly determined by the accuracy of the sensor, with little increment in the error introduced by the mapping algorithm.

In this article, we present results that demonstrate the utility of close range photogrammetry in the measurement of decks in recreational craft as an alternate measurement system to the one based on direct acquisition of coordinates. The areas of deck covered with teakwood for aesthetic or security reasons were measured. Both methods were compared in terms of precision of measurements, time consumption, equipment cost, and ease of manipulation and equipment transportation. Based on the results, we conclude that photogrammetry has advantages in almost every aspect with respect to the direct method. Consequently, photogrammetry is suggested as a suitable method for coordinate measurement of decks in recreational ships. However, in some special circumstances, where ships have wide corridors with few obstacles the direct method can be more appropriate than the photogrammetric method.

This paper presents an optical measuring system based on low cost, high resolution digital cameras. Once the cameras are synchronised, the portable and adjustable system can be used to observe living beings, bodies in motion, or deformations of very different sizes. Each of the cameras has been modelled individually and studied with regard to the photogrammetric potential of the system. We have investigated the photogrammetric precision obtained from the crossing of rays, the repeatability of results, and the accuracy of the coordinates obtained. Systematic and random errors are identified in validity assessment of the definition of the precision of the system from crossing of rays or from marking residuals in images. The results have clearly demonstrated the capability of a low-cost multiple-camera system to measure with sub-millimetre precision.

Este artículo muestra estos procesos para el caso particular de objetos con superficies complejas: el modelado 3D de objetos y su desarrollo a modelos 2D. Se describe el procedimiento para obtener modelos CAD 3D precisos de objetos con superficies complejas a través de fotogrametría terrestre digital; se realizan variasconsideraciones metodológicas que permitirán obtener resultados con alta precisión y minimizado de los tiempos de procesado. Además fue programada una aplicación software para el desarrollo de superficies complejas en el plano. La aplicación proporciona automáticamente archivos CAD estándar, definiendo patrones planosoptimizados necesarios para los sistemas CNC. El cálculo de estos patrones planos está basado en la solución de un problema de optimización que tiene en cuenta la curvatura geodésica de las superficies de curvas isoparamétricas.

In Spain environmental surveillance has mainly relied on measures of selected pollutants in air, water, food and soil. A study was conducted in Madrid to assess the feasibility of implementing a surveillance system of exposure among the general population to specific environmental pollutants, using bio-markers. The project was basically focused on the environment surrounding newborns. Hence, the study population was made up of 145 triplets of pregnant women at around 8 months' gestation, their partners, and newborns from two areas, representing the two main types of urban environments in the region, i.e., the City of Madrid and its outlying metropolitan belt. Multiple biologic substrates were collected from each participant in order to assess the most suitable samples for an environmental surveillance system. The selected contaminants represent the main agents to which a population like that of Madrid is exposed every day, including certain heavy metals, persistent organic pollutants and polycyclic aromatic hydrocarbons, as well as micronuclei in peripheral blood, a commonly used unspecific index of cytogenetic damage. In addition, passive air samplers were placed around subjects' place of residence. This paper reports in detail on the design and response rates, summarizes field work results, and discusses some lessons learned.En España, la vigilancia medioambiental se basa principalmente en medidas de ciertos contaminantes en muestras de aire, agua, alimentos y suelos. En Madrid se ha realizado un estudio para valorar la posibilidad de poner en marcha un sistema de vigilancia de exposiciones a contaminantes ambientales en la población general utilizando biomarcadores. El proyecto ha tenido como eje el estudio del entorno de los recién nacidos. Por tanto, la población de estudio la constituyen 145 «tríos» formados por mujeres en su octavo mes de embarazo, sus parejas y los recién nacidos de dos áreas geográficas, que representan los dos principales entornos urbanos de la región, es decir, Madrid capital y su área metropolitana. Se recogieron múltiples sustratos biológicos de cada participante con el objeto de valorar las muestras más adecuadas para un sistema de vigilancia de exposiciones ambientales. Los contaminantes elegidos representan los principales agentes tóxicos a los que una población como la de Madrid está expuesta diariamente, e incluyen metales pesados, contaminantes orgánicos persistentes e hidrocarburos aromáticos policíclicos; se ha añadido también una medida inespecífica de daño citogenético, los micronúcleos en sangre periférica. Además, se han colocado muestreadores pasivos de aire en los alrededores del domicilio de los participantes. Este artículo describe en detalle el diseño del estudio y la tasa de respuesta, resumen los resultados del trabajo de campo y comenta algunas enseñanzas prácticas de éste.

Mobile LiDAR System is an emerging technology that combines multiple sensors. Active sensors, together with Inertial and Global Navigation System, are synchronized on a mobile platform to produce an accurate and precise geospatial 3D point cloud. They allow obtaining a large amount of georeferenced 3D information in a fast and efficient way, which can be used in several applications such as the 3D recording and reconstruction of complex urban areas and/or landscapes. In this study the Mobile LiDAR System is applied in the field of Cultural Heritage aiming to evaluate its performance with the purpose to document, divulgate, or to develop an architectural analysis. This study was focused on the Medieval Wall of Avila (Spain) and, specifically, the performed accuracy tests were applied in the “Alcazar” gate (National Monument from 1884). The Mobile LiDAR System is then compared to the most commonly employed active sensors (Terrestrial Laser Scanner) for large Cultural Heritage sites in regard to time, accuracy and resolution of the point cloud. The discrepancies between both technologies are established comparing directly the 3D point clouds generated, highlighting the errors affecting the architectural structures. Consequently, and based on a detailed geometrical analysis, an optimization methodology is proposed, establishing a segmented and classified cluster for the structures. Furthermore, three main clusters are settled, according to the curvature: (i) planar or low curvature; (ii) cylindrical, mild transitions and medium curvature; and (iii) the abrupt transitions of high curvature. The obtained 3D point clouds in each cluster are analyzed and optimized, considering the reference spatial sampling, according to a confidence interval and the feature curvature. The presented results suggest that Mobile LiDAR System is an optimal approach, allowing a high-speed data acquisition and providing an adequate accuracy for large Cultural Heritage sites.

Não há como, efetivamente, entender um fenômeno ou processo histórico-geográfico sem se considerar ambos espaço e tempo. Concordando com tal afirmação, os Sistemas de Informações Histórico-Geográficas (HGIS) são cada vez mais utilizados em pesquisas nas áreas da História, da Geografia Histórica e da Cartografia Histórica. No entanto, as pesquisas acadêmicas sobre estes sistemas no Brasil ainda são incipientes. Desta forma, o presente artigo objetiva aproximar este país do contexto internacional quanto a HGIS, apresentando o atual status da pesquisa e seus usos no mundo. Além disso, define o termo em português para o sistema, e busca estabelecer as relações entre as áreas históricas supracitadas e os HGIS. Conclui-se que, embora incipiente, tais sistemas já podem auxiliar estudos nos mais diversos segmentos que buscam entender fenômenos e processos espaço-temporais.

EpiData es un programa informático desarrollado por la Asociación EpiData (www.epidata.dk) que, siguiendo la filosofía de EpiInfo 6, pretende ofrecer todas las ventajas de ese programa: sencillez, aplicabilidad, pocos requerimientos del sistema operativo y de los sistemas de comunicación, ampliándolas con un enfoque claro hacia lo documentación y la calidad de los datos, y las ventajas que para muchos usuarios tiene el entorno Windows®. El objetivo de esta Nota es presentar a los potenciales usuarios las ventajas y limitaciones de EpiData, que puede ser en breve tiempo el equivalente a lo que EpiInfo 6 fue hace unos pocos años.EpiData is an epidemiological software developed by the EpiData Association (www.epidata.dk). Following the EpiInfo 6 philosophy, Epidata, offers all the advantages of EpiInfo 6: simplicity, applicability, few operation and communication system requirements, widening them with a clear focus on data quality and documentation plus the advantages that, for many users, has the Windows® operating system. The aim of this Note is to introduce to potential users the strengths and limitations of EpiData, a software that can become in a short time the equivalent to what EpiInfo 6 was a few years ago.

Durante el trabajo de investigación llevado a cabo en Galicia, en el que tratamos de analizar la integración de las construcciones agrarias en el paisaje, nos hemos encontrado con una realidad que supera, con mucho, a lo que podía uno imaginar: "los millares de construcciones populares insertadas en el paisaje". Este hecho nos complica, a la vez que nos estimula, el trabajo. El estudio de todas y cada una de las construcciones populares de Begonte sería objeto de más de un libro. Por ello nos hemos centrado, tan sólo, en los molinos de agua, conservándose 21 construcciones en este municipio. Esto nos muestra también el enorme potencial de reutilización de construcciones agrarias que presenta Begonte. Existe, al menos, una empresa que se dedica a la reutilización de molinos de agua.