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An essential aspect of pavement construction sustainability is its low-energy consumption and emissions. The study of pavement materials workability tests holds significant importance in terms of achieving well-mixed conditions with low-energy consumption. The complex components of the material and the uncertain kinematic behaviours of aggregates during mixing make this process challenging. And, few studies of the signal response of pavement materials have been found in the field of civil engineering. For this purpose, an accurate evaluation and monitoring approach for mixing are needed. In this paper, a wireless real-time sensing method is used to monitor the dynamic behaviour of aggregates during mixing. A 3D digital twin model, combining data-sensing techniques and numerical simulation, has been proposed for rapid identification of the mixing material. This model has been validated via a data-fusion algorithm. The application of this model makes a contribution to the data-intensive analysing jobs and decision-making tasks in pavement construction engineering. [ABSTRACT FROM AUTHOR]

Mapping Land Use (LU) is crucial for monitoring and managing the dynamic evolution of the human activities of a given area and their consequential environmental impacts. In this study, a multimodal machine learning framework, using the XGBoost classifier applied to attributes constructed from heterogeneous spatial data sources, is defined and used to automatically classify LU in the two French departments of Gers and Rhône. It reaches a mean F1 score of 83 and 86% respectively. This research work also assesses the robustness and transferability of the machine learning model between these two diverse study areas and highlights the challenges encountered, arising mainly from the differences of distribution of the attributes and classes between the study areas. Adding a few samples from the test study area allows the model to learn some specificities of the test study area, and thus improves the results. Moreover, the study evaluates the individual contributions of each data source to the accuracy of predictions of the LU classes, providing insights concerning the relevance of each data source in enhancing the overall precision of the Land Use classification. The findings contribute to a validated LU classification workflow, identify valuable data sources, and enhance understanding of model transferability challenges.

This study assessed the spatio-temporal variation of the degree of curing (DoC) in the Golden Gate Highlands National Park (GGHNP) from 2016 to 2020. The estimation of the degree of grass curing was carried out with the high spatial resolution fused remotely sensed data of MODIS and Sentinel-2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using soil and vegetation moisture content indices-based grassland curing algorithm. Grassland curing indices were computed on Google Earth Engine and converted into Grassland Curing Maps (GCMs), which were then synthesized into fire danger maps. The study showed that all selected indices have the highest DoC in the month of September, with the minor time variation observed in MODIS and Sentinel 2-derived GCI estimated using Shortwave Infrared Water Stress (GCI_SIWSI) in the months of August and March, respectively. The analysis further revealed that GCI_SIWSI (44%) derived from Sentinel-2 data yielded the highest area of extremely high fire danger, followed by GCI estimated using Normalized Difference Moisture Index (GCI_NDMI) (25%) derived from fused data, MODIS-derived GCI_SIWSI and fused-derived GCI estimated using Global Vegetation Monitoring Index (GCI_GVMI) (both 16%). The GCI_SIWSI derived from Sentinel-2 data also showed that more than 95% of the entire landmass of the study area was within a high to extremely high fire danger zone, making the park's vegetation susceptible to fire. Among all the four selected indices correlated against fire point, only SIWSI revealed a positive relationship across MODIS, Sentinel-2 and Fused data. It is also evident from the study that GCMs derived from Fused data outperformed MODIS and Sentinel-2 with the highest R2 and F values of 0.65 and 380, respectively. These results indicate that GCI derived from fused remotely sensed data is a promising GCI for accurately assessing the DoC over mountainous grassland environments. The study paved the way for increasing the spatial resolution for estimating the DoC for fire and fuel management of parks and other fire agencies within mountainous grassland environments. [ABSTRACT FROM AUTHOR]

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance. [ABSTRACT FROM AUTHOR]

Mapping Land Use (LU) is crucial for monitoring and managing the dynamic evolution of the human activities of a given area and their consequential environmental impacts. In this study, a multimodal machine learning framework, using the XGBoost classifier applied to attributes constructed from heterogeneous spatial data sources, is defined and used to automatically classify LU in the two French departments of Gers and Rhône. It reaches a mean F1 score of 83 and 86% respectively. This research work also assesses the robustness and transferability of the machine learning model between these two diverse study areas and highlights the challenges encountered, arising mainly from the differences of distribution of the attributes and classes between the study areas. Adding a few samples from the test study area allows the model to learn some specificities of the test study area, and thus improves the results. Moreover, the study evaluates the individual contributions of each data source to the accuracy of predictions of the LU classes, providing insights concerning the relevance of each data source in enhancing the overall precision of the Land Use classification. The findings contribute to a validated LU classification workflow, identify valuable data sources, and enhance understanding of model transferability challenges. [ABSTRACT FROM AUTHOR]

Although nuclear energy and radioactive materials have considerably impressed national health and economics, inappropriate use of radioactive materials can pose a significant threat to public health and security. This research aims to enhance defensive capabilities for countering nuclear terrorism by accurate detection and continuous tracking. A vital component of this system is to equip surveillance cameras of a region with a relatively low-cost radiation detector (NaI detectors) for counting gamma rays. Data-fusion of the surveillance camera and radioactive sensor that is linked together helps us detect and localize suspicious sources among other objects. The system can provide data flow (continuous) or a collection of snapshots several times (discontinuous), then a fast and new algorithm detects the suspicious source in these two modes. The promising results represent the integrated system by employing the new algorithm to detect the suspicious source in both data modes. Still, the source can be detected quicker in the continuous mode.

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning to accommodate distribution drifts. We propose the Artificial Neural Twin, which combines concepts from model predictive control, deep learning, and sensor networks to address these issues. Our approach introduces decentral, differentiable data fusion to estimate the state of distributed process steps and their dependence on input data. By treating the interconnected process steps as a quasi neural-network, we can backpropagate loss gradients for process optimization or model fine-tuning to process parameters or AI models respectively. The concept is demonstrated on a virtual machine park simulated in Unity, consisting of bulk material processes in plastic recycling. • Integration of process optimization, sensor networks, and continual learning. • Process optimization and continual learning solved by distributed backpropagation. • Differentiable data fusion provides derivates for backpropagation. • Simple integration via process interface. • Demonstration in a virtual plastic sorting process chain. [ABSTRACT FROM AUTHOR]

Abrasive protrusion height (APH) is the core parameter of abrasive tools, and it is also a principal parameter for modeling and simulation of the surface topography of abrasive tools. Because of the difficulty in obtaining the 3D feature information of abrasives and the limited measured data; this study proposes an APH model based on the spatial projection relationship. Combined with the gradient boosting decision tree (GBDT) intelligent algorithm, the APH characteristics of the abrasive tool are quickly generated by the data-fusion learning model. Through the corresponding experiments, we acquired the 2D image of the abrasive tool and extracted the area information of the abrasive projection area. The result shows that the fitting degree R2 of the algorithm model reaches 0.911. Comparing the APH generated based on the algorithm model with the actual one, the average accuracy is about 94.69% and the mean absolute error of generated protrusion height MAE is 5.31 μm, which validates the proposed model. The results of this study demonstrate that the data-driven approach can effectively establish the generation model of the protrusion height of abrasive grains, which not only enable the measurement of the protrusion height of abrasive but also provide a reference for accurate modeling of abrasive tools. [ABSTRACT FROM AUTHOR]

Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides vs sulfonylureas-the two most common oral diabetes medication classes for initial treatment-on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes. [ABSTRACT FROM AUTHOR]

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations. • CAD drawing-based data-driven visual model development in underground mining. • Proposing 3D tile index for visual model reconstruction and interactive visualisation. • Developing interactive visualisation-oriented visual analytics framework for mining visualisation and data fusion. • Developing automation and real-time data visualisation system in underground mining. [ABSTRACT FROM AUTHOR]

Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce.

Motion tracking is a well-defined yet application-specific problem of computer vision field, mostly entailing real-time constraints. Methods addressing such problems are expected also to ensure achievements such as high accuracy and robustness. A probabilistic estimation-based approach is proposed in this paper, in order to enhance the real-time motion tracking process of an RGB-Depth device, in terms of accuracy. A novel method is presented for tracking handpalm of a moving human subject to this end, under a sequence of assumptions such as indoor environment, single object, smooth movement and stable illumination. Tracking accuracy is improved within a particle filter framework by fusing device output with the newly extracted information from RGB and depth images. Experimental results are shared revealing the advantages of the proposed method over the built-in device algorithms. The results demonstrate that the proposed method produces smaller RMSE values both for single implementations and multiexecution trials without violating real-time constraints. [ABSTRACT FROM AUTHOR]

The design of a robust perception method is a substantial component towards achieving underwater human–robot collaboration. However, in complex environments such as the oceans, perception is still a challenging issue. Data‐fusion of different sensing modalities can improve perception in dynamic and unstructured ocean environments. This study addresses the control of a highly maneuverable autonomous underwater vehicle for diver tracking based on visual and acoustic signals data fusion measured by low‐cost sensors. The underwater vehicle U‐CAT tracks a diver using a 3‐degree‐of‐freedom fuzzy logic Mamdani controller. The proposed tracking approach was validated through open water real‐time experiments. Combining acoustic and visual signals for underwater target tracking provides several advantages compared to previously done related research. The obtained results suggest that the proposed solution ensures effective detection and tracking in poor visibility operating conditions. [ABSTRACT FROM AUTHOR]

Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce. [ABSTRACT FROM AUTHOR]

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristic framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy. [ABSTRACT FROM AUTHOR]

This article illustrates a data acquisition methodological process based on Structure from Motion (SfM) processing confronted with terrestrial laser scanner (TLS) and integrated into a Historic Building Information Model (HBIM) for architectural Heritage’s management. This process was developed for the documentation of Cáceres’ Almohad wall bordering areas, a UNESCO World Heritage Site. The case study’s aim was the analysis, management and control of a large urban area where the urban growth had absorbed the wall, making it physically inaccessible. The methodology applied was the combination of: clouds and meshes obtained by SfM; with images acquired from Unmanned Aerial Vehicle (UAV) and Single Lens Reflex (SLR) and terrestrial photogrammetry; and finally, with clouds obtained by TLS. The outcome was a smart-high-quality three-dimensional study model of the inaccessible urban area. The final result was two-fold. On one side, there was a methodological result, a low cost and accurate smart work procedure to obtain a three-dimensional parametric HBIM model that integrates models obtained by remote sensing. On the other side, a patrimonial result involved the discovery of a XII century wall’s section, that had supposedly been lost, that was hidden among the residential buildings. The article covers the survey campaign carried out by the research team and the techniques applied.

Imaging techniques and Image Based-Modeling (IBM) practices in the field of Cultural Heritage (CH) studies are nowadays no longer used as one-shot applications but as various and complex scenarios involving multiple modalities; sensors, scales, spectral bands and temporalities utilized by various experts. Current use of Structure from Motion and photogrammetric methods necessitates some improvements in iterative registration to ease the growing complexity in the management of the scientific imaging applied on heritage assets. In this context, the co-registration of photo-documentation among other imaging resources is a key step in order to move towards data fusion and collaborative semantic enrichment scenarios. This paper presents the recent development of a Totally Automated Co-registration and Orientation library (TACO) based on the interoperability of open-source solutions to conduct photogrammetric-based registration. The proposed methodology addresses and solves some gaps in term of robustness and versatility in the field of incremental and global orientation of image-sets dedicated to CH practices.

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.

The aim of the present paper is to propose innovative approaches able to improve the capability of classical damage indicators in detecting the damage position in linear systems. In particular, starting from classical indicators based on the change of the flexibility matrix and on the change of the modal strain energy, the proposed approaches consider two data fusion procedures both based on the Dempster-Shafer theory. Numerical applications are reported in the paper in order to assess the reliability of the proposed approaches considering different damage scenarios, different sets of modes of vibration and the presence of errors affecting the accounted modes of vibrations.

Foodomics is a newly developed discipline that has become more and more important in the last years where focus on food and the understanding of food systems has increased significantly. In this review, the flow of a typical foodomics study will be followed with a focus on the core components, where chemometric expertise is more deeply involved. These are: how to acquire sound data, how to exploit an experimental design, how to use classification in a proper way, how to look at more analytical platforms at the same time and, not the least, how to understand the limitations when interpreting the developed models. For each of these phases, the most common data issues will be highlighted and some of the most recent chemometric methods that are able to help solving them, will be presented. [ABSTRACT FROM AUTHOR]

A wireless sensor network is characterized by resource constraint and limited computational capable sensor motes that are powered by battery. Ensuring optimal lifetime of a sensor network has always been a research question from the past decade with evidences of various ranges of solutions to mitigate them. However, till now such energy preservation solutions were not found to be effective. Another unique pattern observed from literatures is that majority of such techniques are cluster based which causes latency in data fusion mechanism. Therefore, this paper has discussed a tree-based data fusion technique using an extra module termed as core fusion node, which ensures energy aware and non-redundant data to be fused by the fusion node and then transmit to the base station using multiple-hops. Accompanied with cost effective computation technique, the proposed system (TBFT) is found to outperform conventional LEACH algorithm both with respect to energy and data fusion time, showing the effective outcome till date. [ABSTRACT FROM AUTHOR]

Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research.

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains. [ABSTRACT FROM AUTHOR]

• RGB-based data facilitate biomass LAI and grain yield prediction in chickpea. • Data fusion improves the prediction accuracy and robustness. • The SVM model adequately handled non-linear biomass and LAI data sets. • UAS with RGB camera is a suitable tool for monitoring chickpea growth status. Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date. [ABSTRACT FROM AUTHOR]

• A trajectory reconstruction method using everyday smartphone GPS data. • Data-fusion of trajectories and traditional loop detectors. • Driving lane and lane-changing location become identifiable. • Generating lane-accurate data, over long time periods and network length. • Providing a platform for analyzing vehicles' lateral maneuvers. While lane-changing movements are performed on the entire motorway network for various reasons, such as overtaking, their intensity is substantially greater near complex segments, such as weaving areas. Aside from mandatory lane changes, some drivers also conduct lateral maneuvers for cooperation or anticipation near network nodes. Unlike longitudinal driver behavior (car-following models), lateral driver behavior (lane-changing movements) has received fewer research efforts. The scarcity of suitable data resources to analyze these behaviors and movements might be a crucial cause for this research gap. This paper presents a four-step approach for reconstructing and correcting lateral bias in trajectories collected by a commercial traffic information application running on everyday smartphones. The resulting lateral position is accurate enough to allow for identification of the driving lane, and thus, the lane changes. The algorithm's core is built on a data fusion method using trajectory and loop detector data. The evaluation and validation of the proposed algorithm using drones and closed-circuit television (CCTV) data demonstrate that the core of the algorithm can correctly match more than 94% of trajectory and loop detector data. Between each pair of successive detector stations, the lateral position error has been significantly corrected and reduced to less than half the width of a standard lane of motorway networks. As a result, more than 90% of processed trajectory sample points are in the correct lane. The algorithm requires just two calibration parameters, so it is relatively simple to apply to other test networks. [ABSTRACT FROM AUTHOR]

This exploratory study tested and validated the use of data fusion and machine learning techniques to probe high-throughput omics and clinical data with a goal of exploring the etiology of developmental dyslexia. Developmental dyslexia is the leading learning disability in school aged children affecting roughly 5-10% of the US population. The complex biological and neurological phenotype of this life altering disability complicates its diagnosis. Phenome, exome, and metabolome data was collected allowing us to fully explore this system from a behavioral, cellular, and molecular point of view. This study provides a proof of concept showing that data fusion and ensemble learning techniques can outperform traditional machine learning techniques when provided small and complex multi-omics and clinical datasets. Heterogenous stacking classifiers consisting of single-omic experts/models achieved an accuracy of 86%, F1 score of 0.89, and AUC value of 0.83. Ensemble methods also provided a ranked list of important features that suggests exome single nucleotide polymorphisms found in the thalamus and cerebellum could be potential biomarkers for developmental dyslexia and heavily influenced the classification of DD within our machine learning models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing interests.

Nowadays the necessity to reveal the hidden information from complex data sets is increasing due to the development of high-throughput instrumentation. The possibility to jointly analyze data sets arising from different sources (e.g. different analytical determinations/platforms) allows capturing the latent information that would not be extracted by the individual analysis of each block of data. Several approaches are proposed in the literature and are generally referred to as data fusion approaches. In this work a mid level data fusion is proposed for the characterization of three varieties (Salamino di Santa Croce, Grasparossa di Castelvetro, Sorbara) of Lambrusco wine, a typical PDO wine of the district of Modena (Italy). Wine samples of the three different varieties were analyzed by means of ¹H-NMR spectroscopy, Emission-Excitation Fluorescence Spectroscopy and HPLC-DAD of the phenolic compounds. Since the analytical outputs are characterized by different dimensionalities (matrix and tensor), several multivariate analyses were applied (PCA, PARAFAC, MCR-ALS) in order to extract and merge, in a hierarchical way, the information present in each data set. The results showed that this approach was able to well characterize Lambrusco samples giving also the possibility to understand the correlation between the sources of information arising from the three analytical techniques. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to propose innovative approaches able to improve the capability of classical damage indicators in detecting the damage position in linear systems. In particular, starting from classical indicators based on the change of the flexibility matrix and on the change of the modal strain energy, the proposed approaches consider two data fusion procedures both based on the Dempster-Shafer theory. Numerical applications are reported in the paper in order to assess the reliability of the proposed approaches considering different damage scenarios, different sets of modes of vibration and the presence of errors affecting the accounted modes of vibrations. [ABSTRACT FROM AUTHOR]

The use of damage indicators based on changes of the systems' dynamic properties before and after the occurrence of damage, generally represents a good tool in detecting the presence, the position and also the severity of the damage itself. However, signal noises, difficulties to excite systems for identifying the significant modes of vibrations, particularly the ones more sensible to the damage scenarios and also the presence of multiple damage locations can particularly affect the efficiency of these indicators. In recent years, new algorithms and innovative techniques devoted to improve the efficiency of the classical damage indicators have been carried out in many studies. In this context, the paper presents an approach for damage identification of linear systems developed by combining the use of classical damage indicators based on the modal strain energy (MSE) with a multi-stage data-fusion procedure. In particular, modal strain energy change ratios (MSECR) are evaluated by accounting different sets of modes of vibration. Then, the obtained MSECRs are converted in local decisions and involved in a multi-stage data-fusion process which provides indices able to detect the damage location and its extent. The approach provides a significant improvement of the efficiency of the damage indicators particularly in the presence of noises and multiple damages as shown by the numerical applications reported in the paper. [ABSTRACT FROM AUTHOR]

At present the authors are developing the system FASA, an airborne combination of a Fourier Transform Spectrometer and an imaging system. The aim is to provide a system that is usable to investigate and monitor emissions from natural disasters such as wild fires and from volcanoes. Besides temperatures and (burned) areas FASA will also provide concentration profiles of the gaseous combustion products. These data are needed to improve the knowledge of the effects of such emissions on the global ecosystem. The paper presents a description of the instrumentation, the data evaluation procedure and shows first results of retrieval calculations based on simulated spectra.