bluuebunny/crossref_metadata_2025 huggingface dataset:
Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe
Authors: Le Page Michel , Nguyen Thang , Zribi Mehrez , Boone Aaron , Dari Jacopo , Modanesi Sara , Zappa Luca , Ouaadi Nadia , Jarlan Lionel
Published: March 2023

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases.
Landslide Detection Using Time-Series InSAR Method along the Kangding-Batang Section of Shanghai-Nyalam Road
Authors: Yi Yaning , Xu Xiwei , Xu Guangyu , Gao Huiran
Published: March 2023

Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world.
A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors
Authors: Arslan Niyazi , Majidi Nezhad Meysam , Heydari Azim , Astiaso Garcia Davide , Sylaios Georgios
Published: March 2023

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.
RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning
Authors: He Jianbiao , Xiang Wei , Zhang Qing , Wang Bang
Published: March 2023

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.
An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
Authors: Wang Neng-Chung , Tsai Ming-Fong , Lee Chao-Yang , Chen Young-Long , Wong Shih-Hsun
Published: March 2023

In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.
MTGEA: A Multimodal Two-Stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment
Authors: Lee Gawon , Kim Jihie
Published: March 2023

Because of societal changes, human activity recognition, part of home care systems, has become increasingly important. Camera-based recognition is mainstream but has privacy concerns and is less accurate under dim lighting. In contrast, radar sensors do not record sensitive information, avoid the invasion of privacy, and work in poor lighting. However, the collected data are often sparse. To address this issue, we propose a novel Multimodal Two-stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment (MTGEA), which improves recognition accuracy through accurate skeletal features from Kinect models. We first collected two datasets using the mmWave radar and Kinect v4 sensors. Then, we used zero-padding, Gaussian Noise (GN), and Agglomerative Hierarchical Clustering (AHC) to increase the number of collected point clouds to 25 per frame to match the skeleton data. Second, we used Spatial Temporal Graph Convolutional Network (ST-GCN) architecture to acquire multimodal representations in the spatio-temporal domain focusing on skeletal features. Finally, we implemented an attention mechanism aligning the two multimodal features to capture the correlation between point clouds and skeleton data. The resulting model was evaluated empirically on human activity data and shown to improve human activity recognition with radar data only. All datasets and codes are available in our GitHub.
EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review
Authors: Saibene Aurora , Caglioni Mirko , Corchs Silvia , Gasparini Francesca
Published: March 2023

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain–computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
A Solar Irradiance Forecasting Framework Based on the CEE-WGAN-LSTM Model
Authors: Li Qianqian , Zhang Dongping , Yan Ke
Published: March 2023

With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively.
Characterization of a Novel Approach for Neonatal Hematocrit Screening Based on Penetration Velocity in Lateral Flow Test Strip
Authors: Zucchini Lorenzo , Ajčević Miloš , Coda Zabetta Carlos Daniel , Greco Chiara , Fernetti Cristina , Moretto Carlo , Pennini Simone , Accardo Agostino
Published: March 2023

Hematocrit (HCT) is a crucial parameter for both adult and pediatric patients, indicating potentially severe pathological conditions. Most common methods for HCT assessment are microhematocrit and automated analyzers; however, developing countries present specific needs often not addressed by these technologies. Paper-based devices can be suitable for those environments being inexpensive, rapid, easy to use, and portable. The aim of this study is to describe and validate against a reference method, a novel HCT estimation method based on penetration velocity in lateral flow test strips complying with the requirements in low- or middle-income country (LMIC) scenarios. To calibrate and test the proposed method, 145 blood samples of 105 healthy neonates with gestational age greater than 37 weeks were collected (29 calibration set, 116 test set) in the range of HCT values (31.6–72.5%). The time difference (Δt) from the whole blood sample loading into the test strip instant till the nitrocellulose membrane saturation instant was measured by a reflectance meter. A nonlinear relation was observed between HCT and Δt and was estimated by a third-degree polynomial equation (R2 = 0.91) valid in 30% to 70% HCT interval. The proposed model was subsequently used to estimate HCT values on the test set showing a good agreement between the estimated HCT and the HCT measured by the reference method (r = 0.87, p < 0.001), with a low mean difference of 0.53 ± 5.04% and a slight trend of overestimation for higher hematocrit values. The mean absolute error was 4.29%, while the maximum absolute error was 10.69%. Although the proposed method did not present a sufficient accuracy to be used for diagnostic purposes, it could be suitable as a fast, low-cost, easy-to-use screening tool especially in LMIC scenarios.
The Influence of Varying Atmospheric and Space Weather Conditions on the Accuracy of Position Determination
Authors: Nowakowski Maciej , Dudek Ewa , Rosiński Adam
Published: March 2023

Today’s technological developments make it possible to use machines to perform specific tasks instead of humans. However, the challenge for such autonomous devices is to precisely move and navigate in constantly changing external environments. In this paper, the influence of varying weather conditions (air temperature, humidity, wind speed, atmospheric pressure, type of satellite systems used/satellites visible, and solar activity) on the accuracy of position determination was analyzed. To reach the receiver, a satellite signal must travel a great distance and pass through all layers of the Earth’s atmosphere, the variability of which causes errors and delays. Moreover, the weather conditions for receiving data from satellites are not always favorable. In order to investigate the impact of delays and errors on position determination, the measurements of the satellite signal were conducted, the motion trajectories were determined, and the standard deviations of these trajectories were compared. The results obtained show that it is possible to achieve high precision in determining the position, but varying conditions, such as solar flares or satellites’ visibility, meant that not all measurements are able to achieve the required accuracy. The use of the absolute method of satellite signal measurements contributed to this to a large extent. To increase the accuracy of positioning by GNSS systems, it is first of all proposed to use a dual-frequency receiver that eliminates ionospheric refractions.
Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring
Authors: Vajs Ivan , Drajic Dejan , Cica Zoran
Published: March 2023

Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO2, PM10, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m3/20.56 µg/m3 for the RMSE, for NO2 and PM10, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments.
Configuration of the Geometric State of Railway Tracks in the Sustainability Development of Electrified Traction Systems
Authors: Kampczyk Arkadiusz , Rombalska Katarzyna
Published: March 2023

The state-space interface of the railway track (track) geometry system with an electrified traction system (ETS) constitutes the geometric configuration that is utilised in this study. Importantly, driving comfort, smooth operation (smooth running), and compliance with the ETS are the desired aims. Direct measurement methods were used in the interaction with the system, especially in regard to the fixed-point, visual, and expert methods. In particular, track-recording trolleys were used. The subjects belonging to the insulated instruments also included the integration of certain methods, such as in the brainstorming, mind mapping, system approach, heuristic, failure mode and effect analysis, and system failure mode effects analysis methods. These were based on a case study and are representative of three real objects, i.e., electrified railway lines, direct current (DC), and scientific research objects (which specifically cover five research objects). The aim of the scientific research work is to increase the interoperability of the railway track geometric state configurations in regard to the sustainability development of the ETS. The results of this work confirmed their validity. By ensuring that the six-parameter defectiveness D6 was defined and implemented, the D6 parameter of the railway track condition was first estimated. The new approach reinforces the improvement in preventive maintenance and reductions in corrective maintenance; moreover, it is an innovative supplement to the existing direct measurement method in the configuration of the geometric condition of railway tracks and in the sustainability development of the ETS via interacting with the indirect measurement method.
Sharding-Based Proof-of-Stake Blockchain Protocols: Key Components & Probabilistic Security Analysis
Authors: Hafid Abdelatif , Hafid Abdelhakim Senhaji , Makrakis Dimitrios
Published: March 2023

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from a limited ability to scale (i.e., low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding has proved to be one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e., good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. We then briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. We achieve a number of years to fail of approximately 4000 in a network of 4000 nodes, 10 shards, and a shard resiliency of 33%.
Modern Synergetic Neural Network for Synthetic Aperture Radar Target Recognition
Authors: Wang Zihao , Li Haifeng , Ma Lin
Published: March 2023

Feature extraction is an important process for the automatic recognition of synthetic aperture radar targets, but the rising complexity of the recognition network means that the features are abstractly implied in the network parameters and the performances are difficult to attribute. We propose the modern synergetic neural network (MSNN), which transforms the feature extraction process into the prototype self-learning process by the deep fusion of an autoencoder (AE) and a synergetic neural network. We prove that nonlinear AEs (e.g., stacked and convolutional AE) with ReLU activation functions reach the global minimum when their weights can be divided into tuples of M-P inverses. Therefore, MSNN can use the AE training process as a novel and effective nonlinear prototypes self-learning module. In addition, MSNN improves learning efficiency and performance stability by making the codes spontaneously converge to one-hots with the dynamics of Synergetics instead of loss function manipulation. Experiments on the MSTAR dataset show that MSNN achieves state-of-the-art recognition accuracy. The feature visualization results show that the excellent performance of MSNN stems from the prototype learning to capture features that are not covered in the dataset. These representative prototypes ensure the accurate recognition of new samples.
Optical Penetration of Shape-Controlled Metallic Nanosensors across Membrane Barriers
Authors: Da Ancheng , Chu Yanan , Krach Jacob , Liu Yunbo , Park Younggeun , Lee Somin Eunice
Published: March 2023

Precise nanostructure geometry that enables the optical biomolecular delivery of nanosensors to the living intracellular environment is highly desirable for precision biological and clinical therapies. However, the optical delivery through membrane barriers utilizing nanosensors remains difficult due to a lack of design guidelines to avoid inherent conflict between optical force and photothermal heat generation in metallic nanosensors during the process. Here, we present a numerical study reporting significantly enhanced optical penetration of nanosensors by engineering nanostructure geometry with minimized photothermal heating generation for penetrating across membrane barriers. We show that by varying the nanosensor geometry, penetration depths can be maximized while heat generated during the penetration process can be minimized. We demonstrate the effect of lateral stress induced by an angularly rotating nanosensor on a membrane barrier by theoretical analysis. Furthermore, we show that by varying the nanosensor geometry, maximized local stress fields at the nanoparticle–membrane interface enhanced the optical penetration process by four-fold. Owing to the high efficiency and stability, we anticipate that precise optical penetration of nanosensors to specific intracellular locations will be beneficial for biological and therapeutic applications.
In Situ Calibration of Wetlabs Chlorophyll Sensors: A Methodology Adapted to Profile Measurements
Authors: Salaün Joëlle , Menn Marc Le
Published: March 2023

Measurement of chlorophyll a content in the ocean is essential for biomass assessment, finding the optical properties of seawater, and calibration of satellite remote sensing. The instruments used for this purpose are mostly fluorescence sensors. The calibration of these sensors becomes a crucial point to ensure the reliability and quality of the data produced. The technology of these sensors is based on the principle that a concentration of chlorophyll a in µg per liter can be calculated from an in situ fluorescence measurement. However, the study of the phenomenon of photosynthesis and cell physiology teaches us that the yield of fluorescence depends on many factors that are difficult or impossible to reconstitute in a metrology laboratory. This is the case, for example, of the algal species, its physiological state, the greater or lesser presence of dissolved organic matter in the water, the turbidity of the environment, or the surface illumination. What approach should be adopted in this context to achieve a better quality of the measurements? This is the objective of the work we present here, which is the result of nearly ten years of experimentation and testing to optimize the metrological quality of chlorophyll a profile measurement. The results we obtained allowed us to calibrate these instruments with an uncertainty of 0.2–0.3 on the correction factor, with correlation coefficients higher than 0.95 between the sensor values and the reference value.
Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp
Authors: Liu Zhenfang , Yang Yu , Huang Min , Zhu Qibing
Published: March 2023

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R2, RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.
Development of E-Service Provision System Architecture Based on IoT and WSNs for Monitoring and Management of Freight Intermodal Transportation
Authors: Dzemydienė Dalė , Burinskienė Aurelija , Čižiūnienė Kristina , Miliauskas Arūnas
Published: March 2023

The problems of developing intelligent service provision systems face difficulties in the representation of dynamic aspects of cargo transportation processes and integration of different and heterogeneous ICT components to support the systems’ necessary functionality. This research aims to develop the architecture of the e-service provision system that can help in traffic management, coordination of works at trans-shipment terminals, and provide intellectual service support during intermodal transportation cycles. The objectives concern the secure application of the Internet of Things (IoT) technology and wireless sensor networks (WSNs) to monitor transport objects and context data recognition. The means for safety recognition of moving objects by integrating them with the infrastructure of IoT and WSNs are proposed. The architecture of the construction of the e-service provision system is proposed. The algorithms of identification, authentication, and safety connection of moving objects into an IoT platform are developed. The solution of application of blockchain mechanisms for the identification of stages of identification of moving objects is described by analysing ground transport. The methodology combines a multi-layered analysis of intermodal transportation with extensional mechanisms of identification of objects and methods of synchronization of interactions between various components. Adaptable e-service provision system architecture properties are validated during the experiments with NetSIM network modelling laboratory equipment and show their usability.
Abnormal Behavior Monitoring Method of Larimichthys crocea in Recirculating Aquaculture System Based on Computer Vision
Authors: Wang Zhongchao , Zhang Xia , Su Yuxiang , Li Weiye , Yin Xiaolong , Li Zhenhua , Ying Yifan , Wang Jicong , Wu Jiapeng , Miao Fengjuan , Zhao Keyang
Published: March 2023

It is crucial to monitor the status of aquaculture objects in recirculating aquaculture systems (RASs). Due to their high density and a high degree of intensification, aquaculture objects in such systems need to be monitored for a long time period to prevent losses caused by various factors. Object detection algorithms are gradually being used in the aquaculture industry, but it is difficult to achieve good results for scenes with high density and complex environments. This paper proposes a monitoring method for Larimichthys crocea in a RAS, which includes the detection and tracking of abnormal behavior. The improved YOLOX-S is used to detect Larimichthys crocea with abnormal behavior in real time. Aiming to solve the problems of stacking, deformation, occlusion, and too-small objects in a fishpond, the object detection algorithm used is improved by modifying the CSP module, adding coordinate attention, and modifying the part of the structure of the neck. After improvement, the AP50 reaches 98.4% and AP50:95 is also 16.2% higher than the original algorithm. In terms of tracking, due to the similarity in the fish’s appearance, Bytetrack is used to track the detected objects, avoiding the ID switching caused by re-identification using appearance features. In the actual RAS environment, both MOTA and IDF1 can reach more than 95% under the premise of fully meeting real-time tracking, and the ID of the tracked Larimichthys crocea with abnormal behavior can be maintained stably. Our work can identify and track the abnormal behavior of fish efficiently, and this will provide data support for subsequent automatic treatment, thus avoiding loss expansion and improving the production efficiency of RASs.
Solid Particle Swarm Measurement in Jet Fuel Based on Mie Scattering Theory and Extinction Method
Authors: He Limin , Wu Heng , Li Jifeng , Li Bingqiang , Sun Yulai , Jiang Peng , Wang Xiaoxu , Lin Guanyu
Published: March 2023

To overcome the disadvantages of small and random samples in static detection, this paper presents a study on dynamic measurements of solid particles in jet fuel using large samples. In this paper, the Mie scattering theory and Lambert-Beer law are used to analyze the scattering characteristics of copper particles in jet fuel. We have presented a prototype for multi-angle scattered and transmitted light intensity measurements of particle swarms in jet fuel which is used to test the scattering characteristics of the jet fuel mixture with 0.5–10 μm particle sizes and 0–1 mg/L concentrations of copper particles. The vortex flow rate was converted to an equivalent pipe flow rate using the equivalent flow method. Tests were conducted at equivalent flow rates of 187, 250 and 310 L/min. Through numerical calculations and experiments, it has been discovered that the intensity of the scattering signal decreases as the scattering angle increases. Meanwhile, both the scattered light intensity and transmitted light intensity would vary with the particle size and mass concentration. Finally, the relationship equation between light intensity and particle parameters has also been summarized in the prototype based on the experimental results, which proves its detection capability.
Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions
Authors: Belay Mohammed Ayalew , Blakseth Sindre Stenen , Rasheed Adil , Salvo Rossi Pierluigi
Published: March 2023

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted.
LiDAR-as-Camera for End-to-End Driving
Authors: Tampuu Ardi , Aidla Romet , van Gent Jan Aare , Matiisen Tambet
Published: March 2023

The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving commands, e.g., steering angle, as output. However, simulation studies have shown that depth-sensing can make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can output surround-view LiDAR images with depth, intensity, and ambient radiation channels. These measurements originate from the same sensor, rendering them perfectly aligned in time and space. The main goal of our study is to investigate how useful such images are as inputs to a self-driving neural network. We demonstrate that such LiDAR images are sufficient for the real-car road-following task. Models using these images as input perform at least as well as camera-based models in the tested conditions. Moreover, LiDAR images are less sensitive to weather conditions and lead to better generalization. In a secondary research direction, we reveal that the temporal smoothness of off-policy prediction sequences correlates with the actual on-policy driving ability equally well as the commonly used mean absolute error.
Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals
Authors: Namazifard Saina , Subbarao Kamesh
Published: March 2023

The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a widely used research code, namely EEGLAB. A thorough sensitivity analysis of the estimation algorithm to the parameters (such as the number of samples and sensors) in the assumed signal measurement model is conducted. To confirm the efficacy of the proposed source identification algorithm on any category of data sets, three different kinds of data-synthetic model data, visually evoked clinical EEG data, and seizure clinical EEG data are used. Furthermore, the algorithm is tested on both the spherical head model and the realistic head model based on the MNI coordinates. The numerical results and comparisons with the EEGLAB show very good agreement, with little pre-processing required for the acquired data.
Carthamus tinctorius Suppresses LPS-Induced Anti-Inflammatory Responses by Inhibiting the MAPKs/NF-κB Signaling Pathway in HaCaT Cells
Authors: Kim So-Yeon , Hong Minji , Deepa Ponnuvel , Sowndhararajan Kandhasamy , Park Se Jin , Park SeonJu , Kim Songmun
Published: March 2023

This study aimed to elucidate the anti-inflammatory activity of C. tinctorius leaves by measuring inflammatory parameters such as nitric oxide (NO) production and mRNA expression of iNOS, interleukin-6 (IL-6), and IL-1β in lipopolysaccharide (LPS)-induced HaCaT cells. Further, the effect of C. tinctorius ethanol extract on the MAPKs/NF-κB signaling pathway was examined in HaCaT cells. The phytochemical profile of the ethanol extract of C. tinctorius leaves was determined using UPLC-QTOF-MS/MS. The results indicated that the ethanol extract of C. tinctorius effectively attenuated LPS-induced secretion of NO, IL-6, and IL-1β in HaCaT cells. Further, LPS-stimulated mRNA and protein expressions of iNOS were decreased by pre-treatment with C. tinctorius ethanol extract at the transcriptional level in HaCaT cells. Moreover, the ethanol extract of C. tinctorius suppressed NF-κB signaling in LPS-induced HaCaT cells. This suppression was mediated by MAPKs/NF-κB signaling, inhibiting the phosphorylation of p38 and p65 in HaCaT cells. However, there is no significant effect on the phosphorylation of JNK by the ethanol extract. The QTOF-MS/MS analysis revealed the identification of 27 components in the ethanol extract of C. tinctorius leaves. The data demonstrate that the ethanol extract of C. tinctorius leaves protects the LPS-induced HaCaT cells by inhibiting the expression of iNOS, IL-6, and IL-1β and suppressing the phosphorylation of the p38, p65, p-JNK via inactivation of MAPKs/NF-κB signaling pathway. These results demonstrate that C. tinctorius leaves may serve as a potential candidate to prevent inflammation-related diseases.
Investigation of the Excretion of Triamcinolone Acetonide and Its Metabolite
Authors: Kwiatkowska Dorota , Wicka Mariola , Bulska Ewa , Kaliszewski Pawel
Published: March 2023

Triamcinolone acetonide (TA) is a synthetic corticosteroid commonly used in medical practice to treat various skin conditions, including eczema, dermatitis, and allergies. It is a highly potent derivative of triamcinolone, with a strength that is about eight times greater than prednisone. Although it is sometimes used by athletes, it is important to note that the World Anti-Doping Agency (WADA) prohibits the use of glucocorticoids in competition when administered via injection, oral (including oromucosal, such as buccal, gingival, or sublingual), or rectal routes. However, they are allowed if administered otherwise, such as via inhalation or topical application to the skin. Anti-doping laboratories generally report Adverse Analytical Findings (AAF) for glucocorticoid group substances when their estimated concentration exceeds 30 ng/mL, with some exceptions such as triamcinolone acetonide, which has a reporting limit of 15 ng/mL. It is important to note that this only applies to the parent compound of specified metabolites. To address interpretation issues that can arise with other glucocorticoids, such as budesonide, the authors of this study investigated whether similar issues occur with triamcinolone acetonide. Specifically, they examined whether therapeutic doses of the commonly used medication Previsone could result in anti-doping rule violations due to the presence of triamcinolone acetonide and its metabolites in urine. The study involved ten healthy volunteers, and the analytical procedure was developed using liquid/liquid extraction, hydrolysis, and LC/MS/MS analysis. The results of the study showed that topical administration of therapeutic doses of Previsone does not pose a threat of anti-doping rules violation, as the excretion of the parent compound does not exceed the reporting limit in urine. Additionally, the concentration of 6β-hydroxy Triamcinolone acetonide was also well below the reporting limit.
Biosynthesized ZnO-NPs Using Sea Cucumber (Holothuria impatiens): Antimicrobial Potential, Insecticidal Activity and In Vivo Toxicity in Nile Tilapia Fish, Oreochromis niloticus
Authors: Elbahnasawy Mostafa A. , El-Naggar Hussein A. , Abd-El Rahman Ibrahim E. , Kalaba Mohamed H. , Moghannem Saad A. , Al-Otibi Fatimah , Alahmadi Reham M. , Abdelzaher Othman F. , Mabrouk Mohamed M. , Gewida Ahmed G. A. , AbdEl-Kader Marwa F. , Hasaballah Ahmed I.
Published: March 2023

In this study, a sustainable and eco-friendly method was used to prepare zinc oxide nanoparticles (ZnO-NPs) using a sea cucumber aqueous extract. Then, ZnO-NPs were characterized by instrumental analysis (UV-vis, HR-TEM, XRD, FT-IR, and DLS) and evaluated for their possible antibacterial, antifungal, and insecticidal activities. Additionally, the toxicity of ZnO-NPs was evaluated in vivo against Nile Tilapia (Oreochromis niloticus). The sea cucumber was collected from the Gulf of Suez (Red Sea) at Al-Ain Al-Sokhna coast in Egypt and identified as Holothuria impatiens. The prepared Hi-ZnO-NPs peaked at 350 nm in UV–Vis spectral analysis. They showed quasi-spherical shaped particles with sizes ranging from 13 nm to 47 nm and a predominate size of 26 nm as indicated by HR-TEM. The XRD pattern of Hi-ZnO-NPs revealed a crystalline phase with an average size of 17.2 nm as calculated by Debye–Scherrer equation. FTIR analysis revealed the possible role of H. impatiens biological molecules in the biosynthesis process of ZnO-NPs. Hi-ZnO-NPs showed a negative zeta potential of −19.6 mV, demonstrating moderate stability. Biosynthesized Hi-ZnO-NPs revealed broad antimicrobial activity against Gram-positive bacteria (S. aureus ATCC 25923 and E. feacalis), Gram-negative bacteria (S. typhi, K. pneumonia and E. coli), and filamentous fungi (Aspergillus niger). Hi-ZnO-NPs demonstrated larvicidal activity against the mosquito, Culex pipiens (LC50 = 2.756 ppm and LC90 = 9.294 ppm), and adulticidal action against the housefly, Musca domestica (LD50 = 4.285 ppm and LD90 = 22.847 ppm). Interestingly, Hi-ZnO-NPs did not show mortality effects against Nile tilapia fish (Oreochromis niloticus), highlighting the potential safety of Hi-ZnO-NPs to highly exposed, non-target organisms. However, histopathological and hematological investigations provided dose-dependent impacts of Hi-ZnO-NPs to Nile tilapia. Overall, data provide an eco-friendly approach for synthesizing novel Hi-ZnO-NPs with multiple biomedical properties and potentially low toxicity to Nile tilapia fish.
Experimental Design and Multiple Response Optimization for the Extraction and Quantitation of Thirty-Four Priority Organic Micropollutants in Tomatoes through the QuEChERS Approach
Authors: Rivoira Luca , Del Bubba Massimo , Cecconi Giasmin , Castiglioni Michele , Testa Valentina , Isola Mattia , Bruzzoniti Maria Concetta
Published: March 2023

The chemical contamination in fruit and vegetables represents a challenging analytical issue, with tomatoes deserving to be investigated as they are fundamental components of the Mediterranean diet. Polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs contamination is of serious concern, due to particulate deposition and to uptake from contaminated soils and water. However, time-consuming, non-simultaneous and/or non-eco-friendly extraction procedures are typically used to investigate organic contamination in tomatoes, with nitro-PAHs that have not yet been studied. Based on these premises, this work reports the development of a QuEChERS-based approach, coupled with gas chromatography/mass spectrometry, for the simultaneous determination of 16 PAHs, 14 PCBs and 4 nitro-PAHs in three tomato cultivars. The effect of dichloromethane, cyclohexane and acetone, as well as of four clean-up phases were studied through the advanced combination of full factorial experimental design and multiple response optimization approaches. The final protocol, based on cyclohexane extraction followed by a double purification step with primary secondary amine and octadecyl silica and a sulfuric acid oxidation, led to 60–120% recoveries (RSD% < 15%). Good repeatability (inter-day precision <15%) and negligible matrix effect (<16%) were confirmed and the protocol was applied to the analysis of real tomato samples purchased in a local market.