Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level. Full article

Gold nanoparticles are widely used in electrosensing. The current trend is to phytosynthesize gold nanoparticles (phyto-AuNPs) on the basis of the “green” chemistry approach. Phyto-AuNPs are biologically and catalytically active, stable and biocompatible, which opens up broad perspectives in a variety of applications, including tactile, wearable (bio)sensors. However, the electrochemistry of phytosynthesized nanoparticles is not sufficiently studied. This work offers a comprehensive study of the electrochemical activity of phyto-AuNPs depending on the synthesis conditions. It was found that with an increase in the aliquot of the plant extract, its antioxidant activity (AOA) and pH, the electrochemical activity of phyto-AuNPs grows, which is reflected in the peak potential decrease and an increase in the peak current of phyto-AuNPs electrooxidation. It has been shown that AOA is an important parameter for obtaining phyto-AuNPs with desired properties. Electrodes modified with phyto-AuNPs have demonstrated better analytical characteristics than electrodes with citrate AuNPs in detecting uric and ascorbic acids under model conditions. The data about the phyto-AuNPs’ electrochemistry may be useful for creating highly effective epidermal sensors with good biocompatibility. Full article

The solid-state ion-selective electrodes presented here are based on the FePO₄:Ag₂S:polytetrafluoroethylene (PTFE) = 1:1:2 with an addition of (0.25–1)% microwave-synthesized hematite (α-Fe₂O₃), magnetite (Fe₃O₄), boehmite [γ-AlO(OH)], and alumina (Al₂O₃) nanoparticles (NPs) in order to establish ideal membrane composition for iron(III) cations determination. Synthesized NPs are characterized with Fourier-Transform Infrared (FTIR) spectroscopy, Powder X-Ray Diffraction (PXRD), and Scanning Electron Microscopy (SEM) with Energy Dispersive Spectroscopy (EDS). The iron oxides NPs, more specifically, magnetite and hematite, showed a more positive effect on the sensing properties than boehmite and alumina NPs. The hematite NPs had the most significant effect on the linear range for the determination of ferric cations. The membrane containing 0.25% hematite NPs showed a slope of −19.75 mV per decade in the linear range from 1.2∙10⁻⁶ to 10⁻² mol L⁻¹, with a correlation factor of 0.9925. The recoveries for the determination of ferric cations in standard solutions were 99.4, 106.7, 93.6, and 101.1% for different concentrations. Full article

Seaports are genuine, intermodal hubs connecting seaways to inland transport links, such as roads and railways. Seaports are located at the focal point of institutional, industrial, and control activities in a jungle of interconnected information systems. System integration is setting considerable challenges when a group of independent providers are asked to implement complementary software functionalities. For this reason, seaports are the ideal playground where software is highly composite and tailored to a large variety of final users (from the so-called port communities). Although the target would be that of shaping the Port Authorities to be providers of (digital) innovation services, the state-of-the-art is still that of considering them as final users, or proxies of them. For this reason, we show how a canonical cloud, virtualizing a distributed architecture, can be structured to host different, possibly overlapped, tenants, slicing the information system at the infrastructure, platform, and software layers. Resources at the infrastructure and platform layers are shared so that a variety of independent applications can make use of the local calculus and access the data stored in a Data Lake. Such a cloud is adopted by the Port of Livorno as a rapid prototyping framework for the development and deployment of ICT innovation services. In order to demonstrate the versatility of this framework, three case studies relating to as many prototype ICT services (Navigation Safety, e-Freight, and Logistics) released within three industrial tenants are here presented and discussed. Full article

Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge is more likely to suffer damage in strong earthquakes. The occurrence of damage reduces the safety of bridges, and can even cause casualties and property loss. For this reason, it is of great significance to study the identification of seismic damage in curved beam bridges. However, there is currently little research on curved beam bridges. For this reason, this paper proposes a damage identification method based on wavelet packet norm entropy (WPNE) under seismic excitation. In this method, wavelet packet transform is adopted to highlight the damage singularity information, the $L_p$ norm entropy of wavelet coefficient is taken as a damage characteristic factor, and then the occurrence of damage is characterized by changes in the damage index. To verify the feasibility and effectiveness of this method, a finite element model of Curved Continuous Rigid-Frame Bridges (CCRFB) is established for the purposes of numerical simulation. The results show that the damage index based on WPNE can accurately identify the damage location and characterize the severity of damage; moreover, WPNE is more capable of performing damage location and providing early warning than the method based on wavelet packet energy. In addition, noise resistance analysis shows that WPNE is immune to noise interference to a certain extent. As long as a series of frequency bands with larger correlation coefficients are selected for WPNE calculation, independent noise reduction can be achieved. Full article

In order to locate historical traces, drone-based Laserscanning has become increasingly popular in archaeological prospection and historical conflict landscapes research. The low resolution of aircraft-based Laserscanning is not suitable for small-scale detailed analysis so that high-resolution UAV-based LiDAR data are required. However, many of the existing studies lack a systematic approach to UAV-LiDAR data acquisition and point cloud filtering. We use this methodology to detect anthropogenic terrain anomalies. In this study, we systematically investigated different influencing factors on UAV-LiDAR data acquisition. The flight parameters speed and altitude above ground were systematically varied. In addition, different vegetation cover and seasonal acquisition times were compared, and we evaluated three different types of filter algorithms to separate ground from non-ground. It could be seen from our experiments that for the detection of subsurface anomalies in treeless open terrain, higher flight speeds like 6 m/s were feasible. Regarding the flight altitude, we recommend an altitude of 50–75 m above ground. At higher flight altitudes of 100–120 m above ground, there is the risk that terrain characteristics smaller than 50 cm will be missed. Areas covered with deciduous forest should only be surveyed during leaf-off season. In the presence of low-level vegetation (small bushes and shrubs with a height of up to 2 m), it turned out that the morphological filter was the most suitable. In tree-covered areas with total absence of near ground vegetation, however, the choice of filter algorithm plays only a subordinate role, especially during winter where the resulting ground point densities have a percentage deviation of less than 6% from each other. Full article

Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled samples are required to support the algorithm of CNNs, and, in the case of a limited amount of labeled samples, this may lead to overfitting. In this article, a novel ResNet-based method is developed to achieve fault diagnoses for machines with very few samples. To be specific, data transformation combinations (DTCs) are designed based on mutual information. It is worth noting that the selected DTC, which can complete the training process of the 1-D ResNet quickly without increasing the amount of training data, can be randomly used for any batch training data. Meanwhile, a self-supervised learning method called 1-D SimCLR is adopted to obtain an effective feature encoder, which can be optimized with very few unlabeled samples. Then, a fault diagnosis model named DTC-SimCLR is constructed by combining the selected data transformation combination, the obtained feature encoder and a fully-connected layer-based classifier. In DTC-SimCLR, the parameters of the feature encoder are fixed, and the classifier is trained with very few labeled samples. Two machine fault datasets from a cutting tooth and a bearing are conducted to evaluate the performance of DTC-SimCLR. Testing results show that DTC-SimCLR has superior performance and diagnostic accuracy with very few samples. Full article

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models. Full article

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive. Full article

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap. Full article

In this work, the development of an electrochemical sensor for melatonin determination is presented. The sensor was based on Sonogel-Carbon electrode material (SNGCE) and Au nanoparticles (AuNPs). The low-cost and environmentally friendly SNGCE material was prepared by the ultrasound-assisted sonogel method. AuNPs were prepared by a chemical route and narrow size distribution was obtained. The electrochemical characterization of the SNGCE/AuNP sensor was carried out by cyclic voltammetry in the presence of a redox probe. The analytical performance of the SNGCE/AuNP sensor in terms of linear response range, repeatability, selectivity, and limit of detection was investigated. The optimized SNGCE/AuNP sensor displayed a low detection limit of 8.4 nM melatonin in synthetic samples assessed by means of the amperometry technique. The potential use of the proposed sensor in real sample analysis and the anti-matrix capability were assessed by a recovery study of melatonin detection in human peripheral blood serum with good accuracy. Full article

A capacitive biosensor for the detection of protein A was developed. Gold electrodes were fabricated by thermal evaporation and patterned by photoresist photolithography. A layer-by-layer (LbL) assembly of thiourea (TU) and HAuCl₄ and chemical reduction was utilized to prepare a probe with a different number of layers of TU and gold nanoparticles (AuNPs). The LbL-modified electrodes were used for the immobilization of human IgG. The binding interaction between human IgG and protein A was detected as a decrease in capacitance signal, and that change was used to investigate the correlation between the height of the LbL probe and the sensitivity of the capacitive measurement. The results showed that the initial increase in length of the LbL probe can enhance the amount of immobilized human IgG, leading to a more sensitive assay. However, with thicker LbL layers, a reduction of the sensitivity of the measurement was registered. The performance of the developed system under optimum set-up showed a linearity in response from 1 × 10⁻¹⁶ to 1 × 10⁻¹³ M, with the limit detection of 9.1 × 10⁻¹⁷ M, which could be interesting for the detection of trace amounts of protein A from affinity isolation of therapeutic monoclonal antibodies. Full article

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers’ MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed. Full article

The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency. Full article

Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively. Full article