In this study, to establish the biomechanical characteristics of commercial vehicle drivers’ muscles and bones while operating the three pedals, a driver pedal-operation simulator was built, and the real-life situation was reconstructed in OpenSim 3.3 software. We set up three seat heights to investigate the drivers’ lower limbs, and the research proceeded in two parts: experiment and simulation. Chinese adult males in the 95th percentile were selected as the research participants. In the experiment, Delsys wireless surface electromyography (EMG) sensors were used to collect the EMG signals of the four main muscle groups of the lower limbs when the drivers operated the three pedals. Then, we analyzed the muscle activation and the degree of muscle fatigue. The simulation was based on OpenSim software to analyze the driver’s lower limb joint angles and joint torque. The results show that the activation of the hamstrings, gastrocnemius, and rectus femoris muscles were higher in the four muscle groups. In respect of torque, in most cases, hip joint torque > knee joint torque > ankle joint torque. The knee joint angles were the largest, and the ankle joint angles changed the most. The experimental results provide a reference for improving drivers’ handling comfort in commercial vehicles and provide theoretical bases for cab design and layout optimization. Commercial vehicle drivers often drive for extended periods, and as car seats and pedals are some of the components with which drivers make direct contact, the design of these affects drivers’ handling comfort ,2. ReasoIn recent years, scholars have used computers to establish musculoskeletal models for biomechanical simulation analysis ,7, commoThe above models effectively reveal the seat and pedal effects on the driver’s musculoskeletal system. However, previous studies have focused more on the driver’s single seat height and single pedal operation without further considering the biomechanical characteristics of the drivers when operating different pedals at different seat heights. For this reason, we used the international mainstream biomechanical analysis software OpenSim to establish a musculoskeletal mechanics model for drivers operating different pedals. Dynamic changes in the lower limb joint angles and joint torque of drivers were analyzed by adjusting seat height, which provides a detailed reference for driver seat and pedal design. We also introduced electromyography (EMG) for comfort evaluation, which can more comprehensively characterize the fatigue state of drivers.Regarding comfort evaluation, this usually includes subjective and objective evaluation . SubjectAs nonlinear and non-stationary signals, EMG signals have a frequency range of 20–500 Hz, with the main energy concentrated between 50 and 150 Hz . HoweverJae-Jun Kim , taking When a person is driving, the driver’s main stress areas are the muscles of the waist, chest, and legs that come into contact with the seat. Pedal operation requires repeated use of the legs and feet, causing a burden and fatigue on the lower limbs. The existing literature provides very little information on fatigue in respect of the leg muscles during prolonged car driving . In thisIn summary, most existing studies have analyzed the impact of a single seat height and single pedal manipulation on the biomechanical characteristics of drivers, and there is little research on the lower limbs of drivers. There is also a lack of quantitative analysis of the impact of different pedal types and seat heights on the comfort of driver lower limb manipulation. Based on this, we established a pedal-manipulation simulator to collect the EMG signals of the main muscles in the driver’s lower limbs and analyzed the muscle activation. We also used OpenSim software to reconstruct the model and analyzed the driver’s joint angle and joint torque. This paper provides insights into the changing rules of musculoskeletal biomechanics and EMG signals for the precise design of vehicle seats and the three driving pedals, as well as quantitative design bases for seat-height adjustments. A simulated driving platform was built using the Vicon infrared camera system to conduct three-pedal operation experiments for drivers in normal vehicle conditions. We selected three groups of different commercial vehicle seat heights. Through the driving simulator, we obtained the kinematics (marker-point 3D coordinates), dynamics , and EMG data of drivers’ lower limbs.We performed noise reduction, rectification, and linearization analysis of the driver’s lower limb EMG data collected during the experiment, analyzed the driver’s leg muscle activation, and thus analyzed the degree of muscle fatigue. Muscle activation is the ratio of muscle electrical signals under the operation state to those under the maximum force state. When muscle activation is low, it indicates a higher level of driver comfort.The driving situation was reconstructed based on OpenSim software. The collected kinematic data (marker-point 3D coordinates) could be used to drive the OpenSim model and reproduce the driver’s operational actions. The reverse kinematics analysis using OpenSim software could analyze the angle of the driver’s lower limb joints. Furthermore, based on inverse kinematics, the collected dynamic data  were used for inverse dynamics analysis to analyze the joint torque of the driver’s lower limbs. Combined with muscle activation, changes in the biomechanical characteristics of the lower limbs when the driver operated the pedals were analyzed.This paper can provide insights into the changing rules of musculoskeletal biomechanics and EMG signals for the precise design of vehicle seats and the three driving pedals, as well as quantitative design bases for seat-height adjustments.In this experiment, a Vicon infrared three-dimensional motion-capture system was used to acquire real-time kinematic data of the driver during the operation process. The system included seven infrared cameras with a sampling frequency of 100 Hz, and the cameras were evenly distributed to ensure that all markers could be identified, as shown in We used a Delsys Trigno Mobile wireless surface electromyography sensor from the United States, as shown in The aim of the experiment was to establish a pedal-operation simulation platform for studying drivers’ biomechanical characteristics.SAE J1516 proposes a method for calculating pedal inclination based on H-point heights (H30). The formula is as follows:The pedal center heights can also be calculated through the pedal inclination angles. Assuming that the distance from the accelerator pedal heel point (AHP) to the ball of the foot (BOF) is L (SAE recommends that L be 200 mm), the pedal center height formula is as follows.The seat heights (H30) were set to 405 mm, 455 mm, and 505 mm, and the layout parameters of the driving simulator are shown in Since the structural layout design of the cab should meet the driving needs of the vast majority of passengers, 95th-percentile males were selected as the research participants based on the data provided in GB10000-88 “Human Body Size of Chinese Adults”, that is, a height of 177.5 cm and a weight of 75 kg. Three participants were selected for the EMG signal experiments, as shown in We measured the body dimensions of the participants before attaching the marker points, which were important factors in determining comfort and essential for constructing skeleton models in the Nexus software. As shown in Research has shown that in the process of pedal operation, the main muscles that generate force and exhibit apparent changes are the rectus femoris, hamstring, gastrocnemius, and tibialis anterior muscles . The hamAfter the participants had relaxed completely, an Aixplorer high-frequency ultrasound imaging system was used to determine the muscle positions of the rectus femoris, hamstring (long head of biceps femoris), tibialis anterior, and gastrocnemius muscles . Muscle activation can be represented by the ratio of the current EMG signal to the maximum voluntary contraction state EMG signal. After preparation, we conducted MVC calibration experiments on all four muscle groups to collect the EMG signal of each muscle at its maximum output ,28. EachRectus femoris: The participant was sitting, maintaining the knee joint at 90°. The collector applied resistance at the knee joint to prevent the thigh from lifting.Hamstring muscle: The participant lay prone on the platform, raised the lower leg to maintain an angle of 20–30 degrees with the ground, bent the knee vigorously, and applied resistance at the ankle.Tibial anterior: The participant was sitting, maintaining the knee joint at 90°, and the collector applied resistance on the foot surface to prevent the foot from lifting.Gastrocnemius: The participant was sitting, maintaining the knee joint at 90°. The collector applied resistance at the heel to prevent the heel from lifting.Since this experiment needed to reflect the motion state of the driver’s whole body and obtain kinematics data for the whole body, it was necessary to establish a reflective marker system of the subject’s whole body. We referred to the commonly used marker-point system to drive the selection of a fifteen-link multi-rigid body model. The selected marker-point system has 49 marker points, as shown in Seat height was adjusted to H30 = 405 mm, corresponding to pedal inclination and pedal center height. The specific parameters are shown in After hearing the experimenter’s starting command, the participant grasped the steering wheel with both hands, placed the right foot on the accelerator pedal but did not push down, and placed the left foot on the floor for 3 s. The above actions were repeated three times, with the subject resting for over 1 min after each action. The experimenter took the average of three actions as the final calculation value. The experimenter collected the EMG signal, kinematics, and dynamics data of the lower limbs when the pedal was not pushed down and then saved them.The right foot movements of the subject were changed to three sets of actions: pressing the accelerator pedal to half the stroke at a constant speed, pressing to the bottom at a constant speed, releasing the pedal at a constant speed, and placing the right foot on the pedal. Each pedal operating position lasts for 3–4 s. The above two sets of steps were repeated.Seat height was adjusted to 455 mm and then 505 mm. The above three steps were repeated.The pedals were changed to the brake pedal and clutch pedal. The above four steps were repeated.A new subject repeated the above five steps.The experimental steps were as follows:The comfort experiment in respect of lower limb pedal operation for commercial vehicle drivers with different seat heights is shown in 0 represents muscle activation, which is a dimensionless value. EMG represents the electrical signal generated by muscle force in the current state. EMGmax represents the electrical signal generated by the muscle when it is in the maximum state of force, and we used MVC action calibration to obtain the parameter. The calculation process of muscle activation is shown in After collecting the drivers’ EMG signal data, the data were subjected to 25–250 Hz bandpass filtering and 50 Hz notch filtering to remove noise from the experiment. Then, the signal was subjected to full-wave rectification and low-pass filtering. We calculated the driver’s muscle activation based on the EMG data collected from the MVC calibration experiment . The forIn this study, four experimental schemes were designed for pedal operation. In Scheme 1, the driver lightly put their foot on the pedal. In Scheme 2, the driver pressed the pedal at a constant speed to half the stroke. In Scheme 3, the driver pressed the pedal to the bottom at a constant speed. In Scheme 4, the driver relaxed the pedal at a constant speed until the moment before their foot left the pedal. We calculated the mean and standard deviation of muscle activation during pedal operation for three participants. The muscle activation when the drivers operated the accelerator pedal at seat heights of 405 mm, 455 mm, and 505 mm is shown in As shown in The above four muscle groups are the main contributing muscle groups of the drivers’ lower limbs. We gave them the same weight and accumulated the muscle activation of the four muscle groups in the complete operation process under the same seat height. The activation of the lower limbs for a seat height of 405 mm, 455 mm, and 505 mm was 216.13%, 226.53%, and 222.71%, respectively, indicating that the driver’s lower limb comfort was the highest when the seat height was 405 mm.As shown in Using the same procedure used for the accelerator pedal, the activation of the four groups of muscles during complete operation at the same seat height was accumulated. The activation of the lower limbs corresponding to the seat height from small to large  was 314.84%, 275.44%, and 286.41%, respectively, indicating that the driver’s lower limb comfort was the highest when the seat height was 455 mm.As shown in Using the same procedure used for the accelerator pedal, the muscle activation of the four groups of muscles during complete operation at the same seat height was accumulated. The activation of the lower limbs corresponding to the seat height from small to large  was 251.56%, 313.55%, 327.24%, respectively, indicating that the driver’s lower limb comfort was the highest when the seat height was 405 mm.OpenSim software is a free software package developed by Stanford University for visualizing and analyzing human musculoskeletal systems. Based on OpenSim software, we adopted the human body musculoskeletal model proposed by Raabe et al. . The modAs there were differences in height and weight between the selected human whole-body musculoskeletal model and the drivers selected in the experiment, the model needed to be scaled to adjust the size and quality properties of each part of the body. We used the 3D coordinates of the marker points in the static calibration actions to correspond and scale the musculoskeletal model in OpenSim, as shown in The driver pedal-operation environment model also needed a seat, steering wheel, footrest, and pedal model, so after completing model scaling, the environment model in STL format was established. After importing these models into the OpenSim software, it was necessary to apply different constraints and degrees of freedom limitations to the driver’s hands, back, and hips. In order to simulate the force condition experienced by the driver in the actual operating state, it was also necessary to add support points at the driver’s back, pelvis, femurs, left foot, and right heel according to the contact position between the driver and the driving simulator. Finally, we built the scenario as shown in In the pedal-operation experiment, the complete pedal-operation process was as follows: the pedal was stationary, and then the driver pushed down on the pedal to the bottom at a constant speed. After a slight pause, the driver raised the foot to the initial position at a constant speed and stopped the foot on the pedal, finally leaving the pedal.We used the LH-SZ-6W three-dimensional force sensor to collect the corresponding pedal resistance during a complete stepping process, as shown in The model was analyzed using inverse kinematics (IK) and inverse dynamics (ID), respectively, through the 3D coordinates of marker points and 3D forces of the LH-SZ-6W sensors. The angle and torque changes of the hip, knee, and ankle joints during the driver’s operation were inversely solved.It can be seen from At the beginning of contact with the pedal, due to the gravity factor of the lower limbs, each joint of the lower limbs will be subjected to the torque produced by gravity. When first touching the pedal, the lower limb joints will produce torque due to the influence of gravity of the driver’s lower limbs. Since the mass of the driver’s foot and calf is small, the knee–ankle joint produces less torque, and the hip joint produces about 15 Nm torque. In this study, combined with the biomechanical characteristics of drivers’ lower limbs, an EMG experiment for multiple drivers was undertaken to analyze the changes in the drivers’ muscle activation under pedal-operation states. OpenSim simulation was used to analyze the drivers’ joint angles and torque changes under pedal-operation states.When operating the three pedals, the activation of the hamstrings, gastrocnemius, and rectus femoris muscles is higher in the four muscle groups. When the pedal is not pressed , the muscle activation of the tibialis anterior muscle is maximal, and the angles and torque of the ankle joint are minimal. The activation is lower when operating the acceleration pedal, which indicates that lower pedal resistance can make the driver more comfortable. Compared with the acceleration and clutch pedals, when the brake pedal is operated, the hamstring muscle activation is obviously increased, and the hip, knee, and ankle torque is also larger. Under the three seat heights, when the driver operates the accelerator pedal and the clutch pedal, the lower limb muscle activation is lowest at 405 mm. This is because the 95th-percentile male body height and weight is larger, and the lower seat height is more comfortable. When operating the brake pedal, the muscle activation of the lower limbs first decreases and then increases with the seat height. This is because the brake pedal has the largest resistance, requiring more muscle force and force space to operate.The range of joint angle changes is ankle > knee > hip, which is locally different. At a seat height of 505 mm, when operating the brake pedal, the knee angle changes more than the ankle angle. Similarly, at a seat height of 505 mm, when the clutch is operated, the hip angle changes more than the knee joint angle. This is because when the seat height exceeds the optimal handling comfort height of the human body, the driver automatically adjusts their driving position to meet their comfort needs. The overall variation trend of knee and ankle torque during pedal operation is similar. The magnitude of the joint torque when the driver operates the three pedals at different seat heights is hip > knee > ankle.In subsequent studies, we will consider the effects of more cab design parameters  on drivers’ musculoskeletal biomechanical characteristics and introduce the study of drivers’ lumbar spine and upper-body muscles.


Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park.We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species  Although economic impacts of many IAS are not available, the combined impacts are large. The environmental and economic damages of IAS in the United States, the United Kingdom, Australia, South Africa, India, and Brazil estimated to be in excess of US $ 314 billion per year3. IAS economic damage to agriculture, forestry and fisheries, is estimated to be at least EUR 12 billion per year in Europe alone4.Invasive alien species (IAS) are allochthonous species whose introduction and spread cause severe ecological damages to habitats and species, and reduce biological diversity considerably. IAS severely disrupt natural ecosystems and ecosystem services with consequent adverse environmental, economic, social and cultural implications worldwide5. The Italian National Biodiversity Future Centre6 focus its research activities in findings suitable strategies for biodiversity conservation and monitoring in order to reach EU targets. Assessing and monitoring biodiversity and its evolution, to study native species at risk of extinction, and to plan protection and conservation interventions are some of its specific tasks. Mathematical models are effective tools for evaluating the impact of IAS on natural ecosystems of crucial value for human activities such as forests, agricultural soils and the agrifood sector10. Models allow to build scenarios of conservation planning and management. Specifically, we need to model management strategies that incorporate budget constraints and the spatiotemporal spread dynamics of species11. Modelling and field testing in pilot areas, as the Alta Murgia National Park , a Natura 2000 Site, is useful to identify conservation priorities under present and future scenario.Habitat restoration is hard, labour intensive and costly but necessary to safeguard native species from being displaced by IAS because of competition for resources. Nevertheless, improving measures to control IAS impacts is prioritized in current EU biodiversity policy: to address the problem of IAS, reduce and monitor their environmental repercussions, the European Commission approved a specific regulation (Regulation 1143/2014) supporting interventions aimed at prevention, early detection, rapid eradication and management of spreading invasive species. Development and implementation of model-based decision support systems are a key asset for effective eradication and control programmes for IASAilanthus altissima (Mill.) Swingle is an invasive plant species that has significant impacts in Europe and worldwide. It reproduces both by seeds dispersal12 and asexually by vegetative sprouts14. The winged seeds can be dispersed by wind, water and machinery15, while its robust root system can generate numerous suckers and progeny plants. A. altissima typically occurs in very dense clumps, resulting from even-aged seedling establishment or from clonal expansion through root sprouting, and occasionally it grows as widely spaced or single stems. A. altissima grows on a broad range of anthropogenic to natural sites, from stony and sterile soils to rich alluvial bottoms16. Due to its vigour17, rapid growth, tolerance, adaptability and lack of natural enemies, it spreads spontaneously out-competing other plant species and reducing their growth. A. altissima causes serious direct and indirect ecological, economic, functional and aesthetic damages in non-crop areas18. Above all, it poses a significant threat to biodiversity19 and this threat is likely to increase in the future unless robust action is taken at all levels to control its advancement and spread. The costs of controlling and eliminating it amount to billion of euro per year20. Population management is difficult because of the production of numerous basal and radical shoots after cut and for a hardly eradicable root system. The best control strategy is based on a combined use of mechanical and chemical methods23.A. altissima has quickly spread in the Alta Murgia National Park, which is mostly characterized by dry grasslands and pseudo-steppe, with wide open spaces and low vegetation, whose tendency is to be easily invaded. A. altissima causes serious direct and indirect damages to ecosystems, replacing and altering communities that have a great conservation value24, producing severe ecological environmental and economic effects and causing natural habitat loss and degradation. Many plants and infested areas grow in vulnerable natural habitats, on rocky soils, and at forest edges. There are hundreds infested areas  in the park. Its presence is scattered within the park, by roadsides, dry stone walls, inside and around jazzi (antique sheep folders) and ruins, and is also highly present close to wells and pools. Thus, we consider that the infestation covers the whole park area, but the “real” infested area could be a small percentage compared to the total surface. While the species has been planted historically, this no longer occurs and the park regulation forbids its introduction. There is no exact record about the starting date of the infestation but, considering the size of some trees, we assume that it started at least 80–100 years ago because farmers living in the area introduced plants for ornamental purposes or to create shade, not aware of their invasive potential. A. altissima can seriously harm the ecological balance of the park threatening the fragile grassland ecosystem and its native biodiversity. Only active on-going management can ensure the conservation of the wild flora and fauna species of the park. Hence, the LIFE+ Programme, the European Commission financial instrument for environment and climate action, funded the Life Alta Murgia Project  having as main objective the eradication of the invasive exotic tree species A. altissima from the Alta Murgia National Park using innovative and environmentally friendly techniques.In the last decades A. altissima is the lack of detailed and current data on extent and control efficacy. The most recent map of A. altissima presence and distribution in the park is from 2012, and was a deliverable of the Life Alta Murgia project. This map pre-dates the on-going eradication program. Due to these data limitations, we propose the use of a relatively simple mechanistic model, a reaction–diffusion model, validated against the current plant spatial distribution by satellite images detection. We incorporate the effect of an eradication program using a reaction term, which simulates the plant abatement during a control program. Within the approaches proposed in literature for modeling the IAS removal26, we adopt the spatially explicit model9, where the rate of removal is described by an Holling II type response function and the best control strategy is obtained by minimizing the environmental and economic dameges in terms of costs, under the realistic hypotesis of limited resources.One of the challenges involved in modelling control strategies for 27. These models statistically relate field observations to a set of environmental variables  to predict spatial potential suitable distributions indicating the suitability of locations for a target species across a landscape or region. In this work, a ranking procedure is applied to obtain an estimation of the HSI function, based on the presence map of A. altissima and the land cover map in the analyzed area. Moreover, the continuous Boyce index27 is evaluated to validate the results and assess the ability of the estimated HSI map in predicting the presence of the plant.The growth of the species in our model is modulated by an habitat suitability index (HSI) function, which is estimated by using remote sensing data. Habitat suitability models help both in understanding species niche requirements and predicting species potential distributionThe value of our predictive mechanistic model is to provide an automatic tool for an a-priori estimate of the effectiveness of a planned control action under temporal and budget constraints. In this study, the interest was focused on finding the best budget allocation both in space and time for the park area maintenance, which can help determine whether a control policy needs to be improved. We identified the function of the costs and we formulate an optimal control problem with a penalty term.A. altissima, obtained from a data-driven classification approach applied to very high resolution (VHR) satellite images, combined with the park land cover map. The latter is also used for providing a rough estimate of habitat suitability. To calculate the optimal budget allocation from control measures, we implemented a numerical procedure based on composition of implicit and exponential Lawson schemes28 in a forward–backward algorithm30. We developed the web service, COINS (COntrol of INvasive Species), as a decision support tool to be integrated into management practices. Data and workflows are available through a virtual research environment (VRE) and public metadata catalogues. The parameter values used in the model were taken both from literature and expert knowledge and estimated by the Life Alta Murgia Project data. However, uncertainty will be associated with these parameter values, regardless of whether they are selected using expert knowledge or inferred from data. Arbitrary Polynomial Chaos31 is used to assess the effect of the parametric uncertainty on the predictions of the model. So, the primary aim of our analysis is to assess qualitatively the effectiveness of control strategies for the species A. altissima as well as their sensitivity to changes in allocated budget and surveillance efficiency.As result, we developed an automatic tool that can get the plant presence information from the satellite and use that information for predicting the best action of the park manager. Indeed, there is a lot of satellite data that are not being used to its full potential and there are optimisation methods that are powerful, but not being used on-ground. We tried to link the two domains and make their combined solution accessible and useful to managers. Specifically, the model ingests the initial presence density of A. altissima invasive specie was obtained using a set of four, cloud-free, multi-seasonal WorldView-2 (WV-2) satellite imagery, at 2 m of spatial resolution, acquired on May 19th, 2011, October 5th, 2011 . The algorithm was developed in a previous study34. It is based on multi-class discrimination using spectral and context rules provided through the elicitation of prior expert knowledge about agricultural practices, class phenology, spectral and spatial features. In the first stage no training data were needed to produce the output LC map. The input to the first stage were the multi-seasonal WV-2 images available corresponding to: the biomass pre-peak (January), the biomass peak (May), the dry season (July), the biomass post-peak (October). The deciduous vegetation layer, to which A. altissima belongs, was extracted from the LC map obtained as output of the first stage and it was used for the masking of the images to be analyzed in the second stage.The employed technique consisted of a two-stages hybrid classification process: the first applied a knowledge-driven learning scheme to provide a land cover map (LC) including deciduous vegetation and other classes; the second exploited a data-driven classification to discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map from the first stage. Specifically, the first stage of the classification process was based on an object-oriented, knowledge-driven LC classification algorithm within the eCognition framework  output mapping for the invasive species was obtained35. The accuracy assessment protocol38 was adopted in the second stage of the system to evaluate the reliability of the A. altissima mapping. The overall accuracy (OA) and the user’s accuracy (UA) values for A. altissima were 97.96 The second stage of the algorithm was based on a data-driven, pixel-based support vector machine (SVM) classifier fed in input with the WV-2 images pair acquired in July and October (after testing different input configurations) only for those pixels belonged to the deciduous vegetation layer from the first stage. So the A. altissima plants, a median filter was adopted to improve the final overall accuracy (OA) value39. The choice of the filtering increased OA and UA values up to 99.41 A. altissima.An additional processing step was performed by adding a convolution median low-pass filter to the output map. Thus, the levels of detail achieved depended on the filter size used. Although the application of a filtering procedure may remove not only noisy and false positive pixels but also some pixels belonging to isolated www.biosos.eu). The images were co-registered with each other and calibrated to top of atmosphere (TOA) reflectance values. The reference data used for the training of the second-classification stage and for validation of the final output map were collected during the European Life Alta Murgia Project carried out to eradicate the invasive plant species A. altissima40. In the Supplementary Information, we show the binary map of the initial presence (2012) of A. altissima in the analyzed area.The images considered in the present study were provided by the European Space Agency (ESA) under the Data Warehouse 2011–2014 policy within the FP7-SPACE BIO_SOS project  are usually used as predictors for modeling the habitat suitability42.We used remotely sensed data to generate the habitat suitability index (HSI) map of http://www.sit.puglia.it/portal/portale_cartografie_tecniche_tematiche/Cartografie%20Tematiche/UDS), for the same year. Due to the propensity of the plant to grow along the edges of the woods, along roadsides, rows and in the openings of the woods, we extended the 46 LU classes in a Corine Land Cover-like taxonomy with ten more classes defining the edges of: deciduous forests, olive groves, orchards and small fruit farms, early growth forests, mixed coniferous and deciduous forests, coniferous forests, agricultural production units, vineyards, cultivation and complex systems and other perennial crops. All the analyzed LU classes are listed in Fig. Due to the lack of data, in this paper we give a rough estimate of the habitat suitability, starting from the already extracted presence map in 2012 and the Land Use (LU) map available from the Regione Puglia repository  which has been used in the present paper to evaluate the predictive ability of our model. As evidenced in the previous section, to perform the evaluation we divided the original presence and land cover maps in two parts, the upper one used to build our habitat suitability model, the lower one to evaluate the predictive ability. On that last portion of the map we measured the continuous Boyce index, by associating at each land cover class of the map the habitat suitability previously estimated and then calculating the predicted-to expected ratio. The evaluation has been repeated at different resolutions of the original land cover map, starting from 2 m, with the highest values of the Boyce index corresponding to the highest resolution and close to 1. Since different resolutions generate different distributions of land cover classes the degradation of the index is somehow expected. However, the predictive ability of the model is preserved in a considerable range of decreasing resolutions. E.g., at 20 m, ten times more than the original one, which is the resolution used throughout the paper to perform the simulations, we obtain a continuous Boyce index value of 0.71 which still indicates a good predictive ability of the model in spite of the bias generated by the higher resolution.For assessing the predictive power of the applied procedure for estimating the HSI values, based on the presence map of 11. The invasive species abundance, u, at position x and time t is given by:The formulation of the optimal control problem is based on a PDE reaction-diffusion model with logistic growth, that includes a control term that has Holling-II type behavior and a budget constraintD, the diffusivity, which governs how quickly the species disperses. The second term on the right-hand side is a logistic population growth term, where the coefficient r is the intrinsic growth rate of the population and k is the carrying capacity. The term k according to the suitability of the land cover to the growth of the invasive species. The final term represents the species mortality due to a control effort u is modeled by a Holling II-type function A. altissima growth and eradication.The first term on the right-hand side describes collective motion of randomly moving individuals depending on a coefficient The goal that we consider is to minimize the environmental damage over time at the minimum cost, in terms of the resources allocated to the species harvesting. A penalty term is added to take into account the budget constraint The model parameters were estimated on the basis of the expert knowledge of the plant and of the control program applied in 2014–2019 Alta Murgia National Park within the Alta Murgia Project. The parameter estimation and the numerical procedure for the approximation of the system, as well as the description of the codes written in R open source language, are given in the Supplementary Information.A. altissima in the area, whose final state is depicted in Fig. We firstly run the PDE model  over a pmentclass2pt{minim31 (aPC) was used to quantify the effect of uncertainties in the habitat suitability scores on the predictions of the model. aPC provides a spectral representation of uncertainty, with the density represented by the expansion:P, the number of terms in the expansion, is a function of p:Arbitrary Polynomial Chaoskth uncertain habitat suitability index. Here we focus on uncertain HSI values for four common land cover classes: agricultural production units; bushes and shrubs; areas with natural recolonisation; natural pastures, grasslands, uncultivated. Assumed Gaussian distributions for these parameters were used in the uncertainty analysis.Given samples of the joint density, P scales rapidly with the number of uncertain parameters and polynomial degree. This phenomenon is referred to as the curse of dimensionality. To mitigate this effect, the aPC formulation used here employs Smolyak’s algorithm45 to assemble a sparse sampling grid on which to evaluate the model. For degree ith of the 8:Determining the coefficients of the expansion through a least squares fitting requires at least een from , P scaleentclass1pt{minimaights in  may be cStarting with the initial density map in Fig. By the end of the program in 2019, the plant had been nearly completely eradicated within the park’s boundaries, as shown in the density map in Fig. A. altissima during the years leading up to 2030. As anticipated, the plant reestablishes within the Alta Murgia National Park and shows an increase outside the perimeter where the eradication program was not implemented.We further simulated the repopulation process after the conclusion of the project by running the PDE model  without 6 is one of the National Centers founded in 2023 under the Italian National Recovery and Resilience Plan, which aims to conserve, restore and limiting the loss of biodiversity in Italy. Assessing and monitoring biodiversity and its evolution and to plan protection and conservation interventions to protect native species at risk of extinction because of IAS spread, are some of its specific tasks. Development and implementation of model-based decision support systems are a key asset for effective control programmes for IAS.The Italian National Biodiversity Future Centre (NBFC)Addressing the complex issue of IAS management is an ongoing challenge. The scale of the problem, with an ever-growing number of invasive species and their territorial expansion, leads to diverse impacts on invaded or vulnerable areas. Different management options, such as prevention, containment, control, or eradication, must be carefully considered based on specific circumstances. Due to the magnitude of the IAS problem and the limited resources available for their control, careful planning is crucial. Cost-effective solutions that minimize negative impacts on socio-ecological systems require in-depth analysis of the extent of invasion and predictions of its evolution over time and space.46. Research shows invasive species cause damages ten times higher than management costs, with damage costs increasing rapidly20. However, insufficient implementation of international agreements is evident from rising global damage costs20. A recent paper discusses economic benefits of managing invasive species, comparing avoided costs with and without intervention, including control costs and benefits forgone47.The issue of management costs compared to damages caused by IAS is gaining attention in the scientific community. IAS exert substantial economic and social consequences, resulting in property devaluation and decreased agricultural productivity. To optimize cost-effectiveness, it is imperative to invest in timely and proactive management measuresA. altissima, a pervasive invasive plant species in the Alta Murgia National Park, a Natura200 site in the Southern Italy, one of the pilot site of Task 4.3 of the NBFC. A. altissima has been chosen as target IAS species because has quickly spread in the Alta Murgia National Park, causing serious direct and indirect damages to ecosystems and natural habitat loss and degradation. A. altissima harms the ecological balance of the park threatening the its native biodiversity. As only an active management can ensure the conservation of the wild flora and fauna species of the park, the European Commission funded the Life Alta Murgia Project to eradicate of this invasive exotic tree species. We made use of the insights gained from the Life Alta Murgia Project to evaluate the costs involved in eradication. Our objective was to identify the most efficient allocation strategy for utilizing the available budget for eradication efforts. To estimate the costs, we accounted for the daily expenses of a single eradication team, encompassing herbicide costs and the required resources for clearing specific areas. For a more comprehensive and detailed cost estimation, please refer to the Supplementary Information.The description of the spatiotemporal dynamics of plant invasive species is an important tool that allows an understanding of natural phenomena and how they should be managed in the short and long term to preserve biodiversity and improve the sustainability and conservation of nature. For this purpose, in this work, a PDE optimal control model with penalty term –2) was  was 2) wA. altissima in the Alta Murgia National Park. Using the presence map for the year 2012 and the estimated HSI map at a resolution of 20 m, we observed an increase in density around the spatial points where the species was present in the PDE model at 2022 , for removing as much as possible the alien plants within the temporal window of the management action.As a second analysis, the model –2) was  was 2) w49. There is also a body of work that links remote sensing directly to management using unmanned aerial systems50. This line of investigation focuses on the potential of utilizing artificial intelligence technologies in agriculture, specifically for effective weed management, but it does not link remote sensing to optimisation routines. On the other hand, some works build on optimal control analyses developed to optimize spatiotemporal resource allocation, but where habitat suitability is already known51. Another recent paper focuses on predicting the spatiotemporal spread of invasive species using elevation-dependent habitat suitability and arbitrary Polynomial Chaos method is applied to assess parameter uncertainty and enhance the accuracy of predictions8. However, the paper does not incorporate any control measures for managing the invasive species. Finally, other work focuses on the issue of imperfect detection and misclassification within spatiotemporal invasive species management and highlights the opportunities available by combining optimisation techniques with remote sensing52.The work presented here combines remote sensing to estimate habitat suitability with spatiotemporal optimal control into a single workflow. Some authors also use very high-resolution WorldView-2 imagery to develop maps of invasive species presence, compared to orthophotos from unmanned aerial vehicles for training and validation54. However, both of these methods have their drawbacks. Mass action interactions, which assume that the rate of removal is proportional to the control effort multiplied by abundance, become unrealistic when dealing with high abundances of invasive species. This unrealistic behavior occurs because the removal rate can become exceedingly large for very large populations of invaders. On the other hand, constant removal models, where the rate of removal is simply proportional to the control effort, have their limitations too. These models are not suitable for small populations of invasive species because the removal rate does not decrease as the population declines. This lack of decrease in the removal rate does not account for the increased search time required to find individual invasive species as their population becomes rare. To achieve a more balanced and realistic representation of invasive species removal, we used a Holling-II function response in Eq.  estimation from remote sensing data, has proved to be a valuable tool for predicting the density of invasive species and suggesting optimal allocation strategies for eradication programs. This approach is adaptable and transferable, making it applicable to different invasive species and ecosystems. The model’s optimization process considers factors such as timing and location of control actions, leading to more efficient and cost-effective management strategies. Furthermore, the model allows for early detection and monitoring of re-infestation, enabling managers to take timely action and prevent the resurgence of invasive species.While the eradication programs in protected areas without physical barriers may not achieve long-term success, the model can still provide valuable insights into resource allocation and containment efforts. Future developments of the model could incorporate physical barriers and address other challenges, such as wind or current effects, to enhance its applicability in different scenarios.Supplementary Information.


This type of corrosion occurs in a specific area of a metallic structure and causes holes formation leading to structural failure. A Custom 450 stainless steel sample, placed in 3.5 wt% NaCl solution and applied to a There is an increasing interest in non-destructive and real-time high-resolution approaches for corrosion studies in metals. In this paper, we propose the dynamic speckle pattern method as a low-cost, easy-to-implement, and  Once a corrosive environment is combined with a stressconcentrating environment, stress corrosion cracking (SCC) may occur. Characterization of the progression of the damage is essential for increasing safety and minimizing the economic cost of the system2. Experimental methods, such as eddy current, electrochemical measurements3, optical microscopy, scanning electron microscopy (SEM), x-ray diffraction6, atomic force microscopy (AFM)7, and digital holography8 can provide useful information about pitting corrosion. When a coherent light illuminates a rough sample, a dynamic speckle pattern forms such that any movement in either the exterior surface or interior structure of the sample under study can change that over time. The statistical analysis of such speckle patterns provides important information about the dynamic sample10.The chloride ion  and digital speckle correlation (DSC), for the detection of corrosion, pitting, and crevice corrosion23. Fricke-Begemann et al.24 examined the micro-topography changes of a metal surface during a corrosion process using decorrelation of scattered speckle fields. Moreover, surface corrosion processes of an iron (Fe) immersed in sulfuric acid was studied utilizing digital speckle pattern interferometry (DSPI) by Andrés et al.25.There is an increasing interest in using dynamic speckle pattern method in life and material sciencesquasi in-situ approach for quantitative evaluation of corrosion, which may not be achieved by conventional microscopy systems.In this paper, we introduce utilization of dynamic speckle pattern approach for quantitative assessment of pitting corrosion in a metallic sample. In order to prove the capability and efficacy of the proposed method, dynamic speckle patterns analysis is performed for the real-time monitoring of pitting corrosion in a Custom 450 stainless steel sample. The significant feature of this work is introducing a simple 27. The sample was mechanically abraded by a series of wet silicon carbide sandpapers with grit sizes of #100, #220, #400, #600, #800, #1000, #2000, and #3000. Then, it was polished with a 2.5 A piece of Custom 450 stainless steel was harvested from a frame-type gas turbine installed in a seaside power plant, specifically, from the hub part of a failed blade which was in the first stage of the compressor blade. A wire electrical discharge machine was used to cut the sample into a desired dimension  was prepared from reagents of analytical grade and twice-distilled water. Potentiostatic and potentiodynamic electrochemical tests were carried out to obtain the pitting corrosion, as well as its time and potential, according to ASTM G53. The experiments were designed for the real-time monitoring of pitting corrosion at the maximum bending region. To this end, a potential of 350 Figure mentclass2pt{minimmentclass2pt{minimIn order to prevent corrosion of the part connected to the working electrode and ensure proper placement of device connections in the circuit, the part must be kept out of the solution during testing. This means that the sample cannot be positioned horizontally in the solution under the microscope, as the electrode-connected part would be submerged. To maintain the part out of the solution, the sample must be viewed vertically within the solution during the testing process. To achieve this, a digital camera was connected to a horizontally placed microscope on the table, allowing the container chamber to be positioned vertically and the surface to be magnified and filmed. As a result, the surface can be examined in real-time at the point of maximum bending when the potential is applied and the corrosion process begins. The container used in the experiment is specifically designed using 3 mm thick plexiglass to ensure the sample was placed in a completely vertical position, with proximity to the observed corrosion site. In order to prevent any interference caused by the thickness of the plexiglass, a 0.1 mm lamella was used in place of the cut section. The electrode placement was installed on the lid of the container, and all sides and surfaces of the lamella were insulated using chloroform solution and aquarium glue to prevent solution leakage or movement. To obtain a clear image of the sample surface, the container was carefully re-adjusted rather than relying on microscope settings.Dynamic speckle patterns become more pronounced when an illuminated surface includes any kind of activity. Based on the origins and characteristics of dynamic speckles the knowledge about the inner dynamics of the phenomena may be increased. The more inner dynamics of the samples are known, a better insight can be obtained in controlled experiments and simulations to assess how these dynamics show up in the speckle evolution. The advantage of the presented methodology over the common polymer characterization techniques includes possibility of dynamic and live acquisition of information about the samples. It is a nondestructive and non-contact method and provides spatio-temporal information integration. In addition, this technique is free of phototoxic effects on the sample since a very low laser power is used to illuminate the samples. In the dynamic speckle method, data can be extracted and analyzed instantly within the frame rate limit of the speckle capturing device. It means that the evolution of the sample can be obtained at any moment. The He-Ne laser possesses sufficient coherency and stability. The laser is switched on at least half an hour before the experiment to ensure intensity stability, which is of high importance in the present method. The laser is kept on during the experiments and by using a laser shutter 34. The activities of the pitting corrosion can be attributed to the changes in the surface roughness. The numerical processing of recorded speckle patterns includes statistical parameters which are described in this section.The sample activity, specifically in dynamic materials, can be expressed by various metrics. The main goal of the present work was to characterize the pitting corrosion activity as a function of time. The spatial and temporal coherence of the laser illumination makes it possible to preserve the stability of the speckle pattern for static scatterers. Hence, the observable activity in the dynamic laser speckle can be associated to internal characteristics, such as scaffold activity, growth and cell division, lipid phase separation, movement of cytoplasmic and biochemical reactions, and water-related activities36. The time history speckle pattern (THSP) as a two-dimensional matrix presents M points set evolution through time in sequential speckle patterns are termed image data pack points. In the THSP matrix, the rows represent points set in a speckle pattern, and the columns represent the time. From the initial speckle pattern, the set of random M points was selected such that the first column of the THSP was reconstructed. The other THSP matrix columns were constructed by the equivalent points in consecutive patterns. In order to have the sample activity level as the graphical sign, the THSP was provided such that more line variation of the THSP presents the high activity of the sample under examination. As shown in Fig. Activity-related measurements were performed while the pitting corrosion was initiated by applying a potential of 350 entclass1pt{minima39. The co-occurrence matrix (COM) can be considered as the intermediary matrix which is graphical and is used for the assessment of the consecutive pixels’ dispersion in a THSP of M points which surveys N samples. COM illustrates a histogram related to intensities evolutions:The THSP concept is basically used for numerical calculations such as co-occurrence matrix (COM), and inertia moment (IM) to name a few. Additionally, more parameters are possibly being investigated like motion history image (MHI), and roughness parameters  without the need for THSP43. This image looks to include the essential information to determine how a surface object has evolved during the activity. This parameter is an indicator for the activity of a pixel, which has absolute intensity jump higher to U. This method generates the last image by means of the each pixel  following in the MHI. The comparison indicator for sequential images changes of objects is done by the below equation:k. However, the image which is resulted from the subtracted images, presented by Eq.  and the number of images (N) in a row of the data pack were used to perform sample analysis. Figure n in Eq. :5\documeQ and P indicates the vertical and horizontal speckle pattern dimensions, p and q include the numbers of pixel counter and I as the intensity of whole the speckle patterns. The usefulness of such metrics could be providing a common estimation of distribution roughness. Equivalent, skewness 46. Kurtosis Besides, parameters related to roughness like various moments of the intensities deviated from the mean values, in all patterns are possible to be calculated via the speckle intensity patterns through statistical processing. The expression of average intensities variations to mean value data is as , it is the rough surface that produces the speckle pattern when illuminated with a laser beam due to the scattering of light rays. Similar trends of the two different parameters for various samples have already been studied and reported in referencesIn order to subtract the applied electrical forces as well as the environment around the sample, we conducted control experiments. The results of a typical control experiment are presented in Fig. Our method of dynamic speckle pattern analysis for pitting corrosion is an online and real-time method in the pitting corrosion growth evolution, including the high-resolution characteristic that is of utmost use for a similar dynamic phenomenon. Most importantly, the experimental assessment as a control has been performed which verifies our results.In summary, we presented a novel low-cost method for real-time monitoring of pit initiation and growth in a two-point bending specimen. The experiments were carried out on a Custom 450 stainless steel sample placed in 3.5 wt% NaCl solution. The evaluation of pitting corrosion, including its surface features, was performed based on dynamic speckle pattern analysis. Dynamic growth measurement of pits was obtained through the examination of morphological and various statistical parameters of the surface. Changes in surface roughness showed an increased corrosion-related surface activity. The results showed that the starting point and growth rate of the pits gradually change over time so that new ones are formed each time.


A 50-year-old Caucasian man arrived at the emergency department presenting paucisymptomatic atrial fibrillation. Once discharged after the appropriate treatments, the patient continued to have paucisymptomatic episodes. For this reason, he was provided with the Cardionica device which made it possible to better investigate the type of arrhythmic episodes, in order to tailor his therapy and to finally restore a normal sinus rhythm in the patient. Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide, and it is the arrhythmia that most often requires hospitalization of the affected patient . The datIn a non-clinical environment, the monitoring of heart rate and/or ECG monitoring is not straightforward, and thus the drug dosage can take several days or weeks before being optimized. In this context, a self-managed daily ECG can help in monitoring the cardiac patient’s status, in terms of heart rate and ECG. Modern technology now offers various devices that allow the recording of a one-lead ECG signal, made directly by the patient, at any time and in any place ,5. Some The Cardionica is a brand-new device, intended to be used daily by a re-usable ECG electrode to check for the presence of arrhythmias such as atrial fibrillation, tachycardia and bradycardia . The CarWe hereby describe a case report of a patient diagnosed with AF for whom the Cardionica was decisive in optimizing their therapy.The Cardionica  is a medical device which detects episodes of atrial fibrillation when placed on the thorax by an adhesive electrode. It is intended for use by healthcare professionals and by any adult subject who wants to monitor atrial fibrillation episodes on an intermittent basis. The device acquires a 1 min-long ECG signal from one adhesive and re-usable electrode and processes the signal in real time. It automatically and autonomously detects episodes of atrial fibrillation, tachycardia and bradycardia, and it can be used by the patient frequently (one or more times a day), increasing the possibility to detect arrhythmic episodes. The Cardionica can be used either as a stand-alone device or in connection with a Bluetooth smartphone/tablet . In stand-alone mode, the Cardionica provides 4 types of response using color indicators: a red heart icon in the presence of an AF episode, a yellow heart icon in the presence of tachycardia or bradycardia, a green ‘OK’ icon in the absence of an AF episode and a yellow double arrows icon if it cannot perform the analysis. When used in app-mode, the device shows the ECG trace and the response of the analysis on the smartphone/tablet screen. The app also allows the downloading of data of tests performed in stand-alone mode and enables the ECG traces available as an edf file to be shared. In addition, a pdf report can be generated and shared for each test. With respect to the currently available medical-grade consumer ECG devices, the Cardionica device has 2 novel characteristics. First, it is the only device to use a reusable ECG electrode. The use of an ECG electrode always guarantees acceptable recording quality compared to what happens in devices that use the fingers, in which the quality of the ECG trace is often poor and leads to inconclusive tests . Also, tIn September 2021, a 50-year-old Caucasian man  arrived at the emergency department presenting atrial fibrillation with a paucisymptomatic high ventricular response (150 bpm) of undated onset and severe left ventricular dysfunction (EF < 25%), NYHA class 3. The diagnostic hypothesis was congestive heart failure (CHF) caused by tachycardiomyopathy, and thus the patient was admitted to the coronary intensive care unit with infusion diuretic therapy and therapy for heart failure . Electrical cardioversion was not attempted at the admission, since the onset of AF was not known.In the following days, there was an improvement in heart failure and a reduction in the mean ventricular response to AF (about 95 bpm). Coronary angiography showed no significant epicardial vessel disease.Since the transesophageal echo examination showed no thrombi, two cardioversions at 200 J were attempted, after conditioning with amiodarone, which proved to be ineffective. Subsequently, the presence of fibrosis was excluded by performing an MRI examination. The patient was then transferred to the rehabilitation unit, and he was discharged after 30 days. At discharge, the patient had AF with a ventricular response of 80 bpm. The therapy on discharge included ARNI, beta-blockers, antialdosterones, anticoagulants and amiodarone in view of a possible subsequent attempt to restore the sinus rhythm. At follow-up visits, spontaneous recovery of sinus rhythm was found with an average heart rate of 65 bpm. No noteworthy changes in ventricular repolarization were found on the ECG. Improved LV dysfunction from previous values persisted on the echocardiogram, with an EF of approximately 40%. Subsequently, paucisymptomatic episodes of elevated heart rate detected during self-measurement of blood pressure were reported by the patient to the treating cardiologist. In order to diagnose the type of arrhythmic episodes, the patient was provided with the Cardionica device with instructions to perform ECG recordings twice a day or in the presence of symptoms.The patient was asked to use the device in stand-alone mode until the next follow-up visit, for a total of 6 days. The patient followed the doctor’s instructions, making at least 2 recordings a day. On some days, he also used it in the presence of symptoms, for a total of 15 recordings. After 6 days, at the follow-up visit, the cardiologist checked all the recordings and found intermittent episodes of atrial fibrillation with an average ventricular response of 140 bpm . All ECGThe cardiologist then decided to titrate the beta-blocker therapy. This dose optimization allowed the cardiologist to obtain better control of the average ventricular response during the arrhythmic episodes, which were later resolved . Table 1The patient is currently in sinus rhythm and no ablation procedure has been planned, since he is well managed with drug therapy.The scientific literature has widely investigated the effectiveness of daily ECG monitoring to detect atrial fibrillation . To our This clinical case demonstrates that the Cardionica has been decisive for establishing the type of arrhythmia of the patient and the correct therapy. Drug dosage optimization could not initially be performed in the patient because of the lack of a clear clinical ECG situation.The use of the holter as an alternative solution for the diagnosis of arrhythmic episodes would probably not have led to such a result, unless an at least 7-day holter was used, with higher costs and less comfort for the patient. Similarly, the use of a loop recorder would have been much less comfortable and more expensive, also in consideration of the fact that the Cardionica can be used for any number of days, until the diagnosis has been made. Other currently available medical-grade consumer ECG devices would have allowed daily ECG monitoring. However, these devices mainly rely on taking the ECG signal from the finger, which often leads to recordings of such poor quality that it is impossible to make a diagnosis . The useThe Cardionica allowed to the cardiologist make a diagnosis and to safely optimize drug therapy using only one electrode, providing ECG traces of good quality and without any discomfort for the patient who used the device in stand-alone mode with no difficulty.The presented case opens the way for the possibility of using ECG personal use devices for purposes other than the identification of arrhythmias, also providing help in tailoring the therapy.Cardionica has proved to be an ideal device for personalized medicine, being economical and without discomfort for the patient. Furthermore, beyond this clinical case, its enormous competitive advantage also lies in its ability to perform self-diagnosis of atrial fibrillation, thus allowing the diagnosis of this intermittent and often asymptomatic pathology and in general the monitoring of AF episodes even in patients under antiarrhythmic and/or anticoagulation therapy. This case report shows the practicality and usefulness of monitoring devices that allow easy access to the patient and the doctor: the more information there is available, the better the treatment that can be offered to patients.


This study proposes a high-efficiency method using a co-prime circular microphone array (CPCMA) for the bearing fault diagnosis, and discusses the acoustic characteristics of three fault-type signals at different rotation speeds. Due to the close positions of various bearing components, radiation sounds are seriously mixed, and it is challenging to separate the fault features. Direction-of-arrival (DOA) estimation can be used to suppress noise and directionally enhance sound sources of interest; however, classical array configurations usually require a large number of microphones to achieve high accuracy. To address this, a CPCMA is introduced to raise the array’s degrees of freedom in order to reduce the dependence on the microphone numbers and computation complexity. The estimation of signal parameters via rotational invariance techniques (ESPRIT) applied to a CPCMA can quickly figure out the DOA estimation without any prior knowledge. By using the techniques above, a sound source motion-tracking diagnosis method is proposed according to the movement characteristics of impact sound sources for each fault type. Additionally, more precise frequency spectra are obtained, which are used in combination to determine the fault types and locations. Industrial equipment nowadays has become increasingly large scale and complex. On the other hand, the concept of intelligent manufacturing involves ongoing improvement of equipment performance and resource efficiency. As a result, timely and accurate machine health monitoring plays a far more important role . BearingHowever, vibration diagnosis still has limitations and cannot work well in cases such as slow rotating and complex dynamics . AcoustiSignal processing techniques based on multi-channel microphones have been presented as a solution to the issue. Spatial distribution information of the acoustic field around the sound sources can be reconstructed through the specific positional relationship between microphones, which allows for the directional enhancement and denoising of signals even from the moving sound sources. Near-field acoustic holography (NAH) and beamforming based on far-field signal models are two efficiently used detection techniques. According to Lu  and Hou The main purpose of the study is to propose a novel sound source motion-tracking approach for fault-type recognition. Spatial domain information is fully made use of, which provides a fresh perspective on fault diagnosis as an addition to the established analysis methods based on time and frequency domains. Sound sources produced by faults for various components and types are distinct from each other in obedience to the operating mechanism, which gives more logical proofs for diagnosis using motion trajectories indicated by direction-of-arrival (DOA) estimates. DOA estimates are also used for the signal beamforming, and frequency spectra with good noise reduction and obvious fault features can be extracted. The motion trajectories and the spectra work together to identify the fault types, as well as the positions; accordingly, the characteristics of the bearing fault acoustic signals are further observed and analyzed. However, precise DOA estimation of sound sources coming from large equipment often needs to be located in both the vertical and horizontal dimensions. For this reason, microphone configurations such as planar or circular arrays are customarily adopted, and a large number of microphones are used to achieve good accuracy; therefore, the practical applicability of the technique is constrained and subject to an expensive cost and high computational time. The study presents an adaptive array signal processing method that provides high-accuracy direction-of-arrival (DOA) estimation and fault feature extraction with fewer array elements, in order to reduce the reliance on the number of microphones in real applications. For the proposed method, a co-prime circular array (CPCA) configuration is attempted for signal acquisition, and the estimation of signal parameters via the rotational invariance techniques (ESPRIT) algorithm is used for the data processing. The peculiar structure of a co-prime array was first proposed and validated by Xiang and Bush in 2015, which makes it possible to construct virtual array elements. By exploiting the second-order statistics, more degrees of freedom are acquired, which overcomes the Nyquist theorem’s restriction on array apertures ,37,38,39In this paper, acoustic signals from a rolling bearing test rig are collected using a uniform circular microphone array (UCMA), as well as a co-prime circular microphone array (CPCMA) simultaneously, where exactly the same number of microphones are applied in both arrays. Three test conditions are investigated, including outer race fault, inner race fault, and rolling element fault. Signals between the two arrays are observed and compared after the ESPRIT processing, and the effectiveness in fault diagnosis is evaluated.Co-prime array configuration was at first applied in linear arrays. Let M and N be a pair of co-prime numbers and M < N, then consider two sparse uniform linear sub-arrays with M and N elements, respectively. Overlap the two sub-arrays, and an M + N − 1 element co-prime linear array can be created, which was validated by experiments to have comparable degrees of freedom to a uniform linear array of MN elements ,37. The x-axis as the reference line passing through Element 1. The angle measured down from the z-axis to OS is the elevation angle x-axis to OS’ is the azimuth angle The array response vector When there are P uncorrelated sources, we have:The received signal then can be written as:Then, the covariance matrix of the signal can be obtained as:For an M + N−1 element UCA, the angle ESPRIT was initially presented based on uniform linear arrays (ULA). ESPRIT exploits the rotational invariance of the signal subspace; accordingly, the channel manifold vectors should follow the Vandermonde structure, while the UCA signal model does not meet the condition. Transformation based on phase mode excitation converts a UCA manifold vector into a ULA-like Vandermonde matrix by using the recursive nature of Bessel functions. Consider To keep Elevation information of the manifold also needs to be converted into a symmetric amplitude taper, by giving the matrix Combined with those aforementioned, the beamformer By considering the phase delay between two adjacent array elements as an artificially defined delay matrix, the array can be split into identical sub-arrays. According to the rotational invariance, the sub-arrays share the same signal subspace, which can be obtained by eigenvalue decomposition of the array signal covariance matrix. Combine the sub-array signals into a matrix The experiment is conducted on a QPZZ-II test rig, as shown in The microphone array is placed at a 170 mm distance in front of the apparatus, as is shown in Firstly, the motion regularity of each fault type needs to be analyzed. The outer race fault produces sounds when balls are passing through the defect, which are always from the same direction. Inner race fault and rolling element fault generate moving sound sources that uniformly rotate with the shaft. Due to the existence of moving sources, the DOA estimation algorithm cannot be applied directly to a long-lasting data section. The collected data need to be chopped into segments in keeping with the real-time location of the defects.Separate the collected signals into UCMA and CPCMA sets.Figure out the rotation angles between every collision for each defect type. Since the outer race is fixed, the azimuth angle values for the outer race defect should always be the same. To calculate the angular interval of the impact sounds for the inner race defect, we have:However, it cannot be guaranteed that every split segment contains information of interest. When there is no collision of defects in the segment, unconcerned sound source positions are likely to be located instead. To address the issue, elevation angles are checked to screen unwanted DOA estimates out, for the impact sounds always come from the circumferences where the defects locate, and the elevation angles from the same circumference have similar values. Azimuth angles then are feasible to be observed to speculate about the fault types. Furthermore, spatial filtering is applied on the basis of the screened DOA estimates, and the resulting frequency spectra also help to determine. The signal processing procedure is carried out as follows:The rolling element fault has the peculiarity that impact sounds are triggered both on the inner and outer races, and both are moving sound sources, for which we have:L denotes the number of snapshots per second, which depends on the sampling rate of signal acquisition.An adequate interval angle 3.Apply ESPRIT to UCMA and CPCMA signal segments to obtain DOA estimates of both. Elevation angles for each fault type should be worked out based on the distance between the defect and the reference element of the microphone array. When an elevation angle estimate has a large difference from the calculated value in a segment, it means the segment gives invalid information and the location should be excluded.4.Compare the azimuth angular intervals of the screened DOA estimates with 5.Apply beamforming for the further determination of fault types.The flow chart in It is noticed that negative values are occasionally found in the azimuth angle estimates, as has been shown in The mono signal is captured by the microphone at the center of the array right in front of the shaft; therefore fault features are greatly masked by the modulation of the shaft rotational frequency The elevation angle for the inner race defect is calculated as 5.29 degrees. For the inner race fault, Similar to the outer race fault circumstance, the fault feature eigenfrequency in the mono signal is overwhelmed by the The rolling element defect successively collides with the outer and the inner races, generating fault locating with two different elevation angles. Based on this, DOA estimates after screening are given in It can be found from the DOA estimates of UCMA that elevation angle values are highly dispersed, and the phase ambiguity occurs more frequently, which shows a further decrease in location accuracy compared with the inner race fault condition. It is probably because the interval time between impacts is so close, which aggravates the aliasing of sound waves and makes locating more difficult. The RMSE values confirm the observation that location errors of UCMA are much larger than that under the inner race fault condition. DOA estimation precision of CPCMA also has a slight decline, which is manifested as a small rise in phase ambiguity occurrence frequency. Regardless of this, CPCMA still maintains an accuracy numerically no less than before.In order to compare the diagnostic efficacy at different rotation speeds, experimental results under the outer and inner race fault conditions at 725 rpm are presented to show the location and fault feature extraction effects of both static and moving impact sound sources. For the outer race fault signal, The comparison shows that at a higher speed, the characteristics of both defect and interference sound sources are enhanced, which is mainly evidenced by the amplitude increase in the high-order harmonics of the fault feature frequencies, as well as the In this paper, a novel diagnosis method based on sound source motion tracking is proposed, and acoustic signals of three different bearing fault types at two rotation speeds are acquired to validate its effectiveness. Compared with vibration signals, acoustic signals have significantly more noise and aliasing problems. Due to the close positions of each bearing component, eigenfrequency modulation of the fault-free components, such as the shaft, has a great effect even on the high-order harmonics and in many cases overwhelms the fault feature frequencies. Sounds of the defects can be directionally enhanced by locating the collisions using a microphone array. However, UCMA requires a large number of microphones to achieve a certain location accuracy. CPCMA takes advantage of the non-uniform differential relationship between array elements to create a virtual array through the vectorization process, which considerably raises the degree of freedom of the array and lessens the dependence on the number of microphones needed for high-precision. The superiority of CPCMA performance with the same number of microphones has been verified by experiments in the study. When applying more microphones, the performance gap between the two array configurations will be larger in accordance with CPCA properties. On this basis, using ESPRIT as the signal processing method, the paired elevation and azimuth angles can be quickly estimated, which can largely improve the process speed for array signals. The CPCMA-ESPRIT method realizes the low-cost and high-efficiency sound source location of defects, and satisfies the requirement for intelligent analysis in practical engineering.


With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time. Vehicle trajectory data analysis is a research hotspot in intelligent transportation and smart city . People Clustering large-scale trajectory data is challenging. First, the trajectory is complex, so the computational cost for clustering analysis is high. Second, the trajectories sampled by the multi-camera joint monitoring system are not equidistant, making it difficult to unify the dimensions between different video object trajectories, so reasonable point correspondence rules should be given to measure the difference in distance calculation. Finally, there is a vision-blind area and a long distance between camera groups, so the direct calculation without simplifying trajectories causes the local differences between trajectories to be overlooked due to the large global scope. The traditional algorithms only cluster the equidistant trajectories of sampling points under a single camera for small-scale datasets without considering the hierarchical relation between camera position, the field of view, and the motion of video objects between cameras and scenarios and without reasonable analysis of cross-group trajectory data. Therefore, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. Based on our previous work, we have done further research in this paper . In thisThis paper is mainly structured as follows: The research fields involved in this paper include video-geographic scenario data fusion organization and trajectory clustering. The research status of relevant fields is as follows.The data fusion organization of video-geographic scenarios is the basis of video object analysis combining geospatial information. Based on the concepts of Multi Media GIS , Geo VidIn recent years, research has focused more on the fusion of video content and geographic scenarios . The fusCommon in pattern recognition, data analysis, and machine learning, trajectory clustering is an efficient method for analyzing trajectory data, aiming to obtain space, time, and even potential information in trajectory data. It is widely applied in such fields as object motion prediction , trafficAccording to the availability of labeled data, there are three trajectory clustering methods: unsupervised, supervised, and semi-supervised models. The unsupervised model aims to cluster the data without human experts supervising or labeling the data and obtain the reasoning function by analyzing the unlabeled dataset . The supThe unsupervised trajectory clustering algorithm does not rely on other prior knowledge but uses constructors to describe the implicit relationship between trajectory samples. The representative methods are densely clustering models , hierarcIn order to understand the three kinds of algorithms more clearly, we give the tabular overview of different trajectory clustering algorithms in Building the mapping relation between each camera image square plane and geographical space object square plane is necessary to transform from image space coordinates to geographical space coordinates. This paper used the contact point between the video object subgraph and the ground as the locating point and sampled at a certain time interval . The vidThis study uses the homography matrix method to construct a mapping model. If the image coordinate of a point is If the mapping matrix is The image plane is scaled, translated, and rotated to a geospatial plane, so the mapping matrix When using the homography matrix method, it is assumed that the camera field of view plane in geographic space is horizontal—that is, Z = 0 at the plane. Thus, the mapping relation between image and geographic space can be regarded as the mapping from one plane to another. To simplify the calculation, the Z in The geospatial coordinates of the video object trajectory can be obtained according to the solution of the matrix The closely adjacent location cameras within the group and the overlapped camera field of view are preferred when cameras are divided into groups. The weigh options rules between the two can be specified asInspired by the dynamic time warping , to bettThe trajectory sequence may not have equidistant time points. A feature space represented by The defined sequence is the point correspondence sequence of optimal unequal-length trajectory:Then, the sequence The distance cost between The optimal total cost between This part explains the increase in distance calculation due to the time overlap of multiple cameras in the camera-joint system. Then it gives a method to obtain the scale factor of trajectory distance overlap to offset the increase in distance.Assuming For According to the capture sequence, the trajectory corresponding to the video object can be expressed as a series of nodes:Assuming the two trajectories In This factor should be multiplied in the distance calculation to eliminate the increase in distance due to camera overlap.This part details the trajectory clustering algorithm within the camera group (SCAIMG) and between camera groups (SCAIBG). The process of the algorithm is shown in Supervised clustering results can be obtained with more additional useful supervised information than unsupervised clustering. For the video object trajectory under the camera joint system in this chapter, the “main camera” of video objects is defined as the label for the supervised clustering of trajectory samples. For the trajectory between camera groups, the number sequence of the camera group passed through is used as the label of the supervised clustering of the trajectory sample.In the camera group of the camera-joint system, the camera number with the most capture times should be the same among video objects with the same characteristics. Therefore, for accurate trajectory data clustering, we use the camera number with the highest capture times corresponding to each video object as the label of supervised clustering:Furthermore, we used the camera group number as the label of supervised clustering between camera groups.Algorithm 1: Supervised trajectory clustering algorithm considering camera information in the multi-camera collaborative monitoring group (SCAIMG)The trajectory clustering algorithm within the camera group aims to obtain a group of clustering centers. First, the video objects in the camera group are spatialized, followed by clustering their trajectory. The pseudo-code of trajectory clustering within the group (SCAIMG) is shown in Algorithm 1.In Algorithm 1, line 11 and line 13 represent the operations of “approaching” and “moving away”, respectively. The schematic diagram of the approaching operation is shown in The trajectory of the “moving away” operation moves in the opposite direction in We can combine the entry and departure points in each camera field of view of video object trajectories to reflect the dynamic characteristics of group trajectories ,49 becauThe sampled trajectory is supposed as:We proposed a trajectory clustering algorithm between camera groups with multi-hierarchies to reasonably analyze the cross-camera group trajectories. The algorithm aims to obtain the Algorithm 2: Supervised trajectory clustering algorithm considering camera information between groups (SCAIBG)The pseudo-code of Supervised trajectory clustering algorithm considering camera information between groups (SCAIBG) is shown in Algorithm 2.This part briefly introduces the experimental conditions and data and the trajectory clustering results within and between camera groups. Compared with traditional clustering algorithms, the advantages of the proposed algorithm are demonstrated by silhouette coefficient evaluation, and the time complexity is analyzed to prove the algorithm’s effectiveness.2. This dataset contains high-definition synchronous videos collected in Dubuque, the United States, including scenarios such as residential areas and expressways.The experimental data in this paper come from the CityFlowV2 dataset  newly laThe experimental data in this paper are all the original dataset’s video sequences. The experimental environment comprises software  and hardware (Intel (R) Core (TM) i7-10510U CPU @ 1.80 GHz 2.30 GHz, RAM 12.0 GB, and NVIDIA GeForce MX250). The algorithm to obtain the video object trajectory by preprocessing is as follows: the video dynamic object detection algorithm is Mask-RCNN , and theThe CityFlowV2 dataset is divided into groups according to Equations (9) and (10). Partial division results are shown in The number of cluster centers is determined using the elbow method . Due to For SCAIMG and SCAIBG, the cluster center value of each camera group is determined using the elbow method, as shown in According to If the initial cluster centers for SCAIMG and SCAIBG are randomly selected, this usually results in slightly different clusters at the end when the clustering algorithm is rerun-ed. In order to solve the above problem, we refer to the work of the other literature , make imAssume that there are First, we demonstrate the visualization results of clustering ten camera groups with overlapping cameras in each group. For example, the visualization effect of the trajectories in group 1 is shown in The trajectories under each camera group were obtained in chronological order. The clustering results of SCAIMG in each group are shown in Through the visualization effect, a small number of cluster centers represent the overall trend of trajectory data of 10 camera groups.In this paper, we performed clustering analysis on trajectory subsegments with three and five hierarchies. There are trajectory subsegments with seven, nine, and eleven or even more hierarchies. However, in these cases, there is a small number of trajectory subsegments, making the clustering analysis lose its practical significance because clustering is to extract a large number of trajectories of the overall trend.The number of the trajectory subsegments at different hierarchies is shown in The results of vehicle trajectory clustering between groups with three hierarchies (upper) and five hierarchies (lower) are shown in The trajectories between groups have a larger geographical range than those within a group. It can be seen from the visualization effect that the SCAIBG has achieved the overall trend of vehicles moving between groups. The green line represents all vehicle trajectories between groups, while the arrows in other colors represent the overall trend of vehicles moving at different hierarchies.The silhouette coefficient  is used For SCAIMG, the point correspondence of trajectory is considered when trajectory clustering within a group is so that the unequal-length trajectories can move reasonably according to the corresponding relation. The distance overlapping scale factor is used to offset the unreasonable distance increase between trajectories caused by camera overlap to obtain accurate trajectory centers. However, the traditional algorithm does not consider the corresponding relation between trajectories. It can only obtain the approximate distance between trajectories when measuring the trajectory similarity, so the cluster center obtained from the clustering results can not accurately represent the overall trend of trajectories. In addition, the camera number with the most capture times is used as the clustering label for supervised clustering, considering the spatial relationship between the camera and the video scenario. For the trajectories between groups, similar to SCAIMG, SCAIBG takes group number as the cluster label for supervised clustering and considers the spatial position of camera groups and the spatial relation of video scenario, so the clustering effect is also better than that of the traditional algorithm, as shown in For SCAIMG, it is assumed that there are graining , so it tTherefore, the algorithm maintains linear time complexity, proving that the algorithm is effective.Under the hardware given, the CPU time required by SCAIBG on the CityFlowV2 dataset is shown in The algorithm can obtain clustering results in a few seconds or even one second in groups other than groups 2 and 3. The longer time in groups 2 and 3 is because some trajectories within the group are long, taking it extra time to calculate the distance. Overall, SCAIMG can quickly extract the cluster center of large-scale vehicle trajectories.For the SCAIBG, the clustering results in Under the hardware given, the CPU time required for different hierarchies of trajectories is shown in SCAIBG can effectively simplify the calculation of regular distance by extracting the start and end points of the trajectory. Therefore, it can be seen from (1)Multi-scale vehicle path prediction. According to priori theories, we can combine time information to analyze the multi-scale prediction of vehicle moving path.(2)Urban Planning and Road Planning. Different levels of clustering results, combined with road constraints, provide effective experience and technical support for urban planning departments and traffic management departments.(3)Trajectory outliers detection. The clustering results can provide a prerequisite for vehicle trajectory outliers detection. Trajectory outliers are a small number of trajectories that are obviously different from other trajectories data in the trajectory data set, so vehicle trajectory outliers can be analyzed through the distance between the vehicle trajectories and clustering centers.The purpose of “Hierarchical Clustering Algorithm for Multi-camera Vehicle Trajectories” proposed in this paper is to represent vehicle video objects with similar motion patterns by obtaining different levels of video object clustering centers as the representative of vehicle video object motion patterns. The method in this paper has and is not limited to the following practical applications:In the above applications, we think that the trajectory clustering algorithm should ensure high accuracy and efficiency. High accuracy is the premise of the accuracy of the follow-up work, and efficiency is the rigid requirement of some practical applications. For example, path prediction analysis is applied to navigation planning and trajectory anomaly detection to security departments. Rapid cluster analysis enables these applications to be implemented safely.In this paper, the elbow method is used to determine the optimal number of clusters in clustering analysis, but this method may have some shortcomings in practical application, because the vehicle trajectory has road constraints, and there is a deviation simply according to the elbow method to determine the clustering center without taking into account the actual situation.Therefore, in the future scientific research work, the final number of clustering centers should be determined by combining the algorithm and road constraints.This paper proposed a multi-camera vehicle trajectory hierarchical clustering algorithm based on spatio-temporal grouping, aiming at the problem that the traditional trajectory clustering algorithm did not consider the camera position information, the field of view, and the hierarchical relation of the video object movement between the camera and the scenario. This method used camera number and group number as labels and was a supervised clustering algorithm considering camera information applied to trajectories within and between groups, respectively, enabling trajectories to be classified into reasonable clustering centers.The experiment showed the clustering results and the comparison and analysis of silhouette coefficients between the proposed method and other clustering methods, and the time complexity analysis proved the proposed algorithm’s accuracy, effectiveness, and high efficiency. The limitation of this paper lies in the lack of trajectory clustering analysis combined with spatial semantic features, which will be improved in future research.