bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Features of the Surface Urban Heat Island of Bacău City (Romania) during the Warm Season and Local Trends of LST Imposed by Land Use Changes during the Last 20 Years
Authors: Sfîcă Lucian , Corocăescu Alexandru-Constantin , Crețu Claudiu-Ștefănel , Amihăesei Vlad-Alexandru , Ichim Pavel
Published: July 2023

Using MODIS and Landsat LST images, the present paper advances a series of results on the characteristics of the surface heat island (SUHI) of Bacău City (Romania) during the warm season (April to September) for a period of 20 years (2001–2020). At the same time, given their higher temporal resolution and their availability for both day and night, MODIS LST was used to understand the spatial features of the SUHI in relation to land use. In this way, a total of 946 MODIS Terra and 483 Landsat satellite images were used to outline the main LST characteristics of the days with clear sky in this middle-sized city in northeast Romania. In order to analyze MODIS LST changes in relation to land use changes in the period 2001–2018, we used the standardized CORINE Land Cover datasets. With the help of the Rodionov test, we were able to determine the geometry and intensity of the SUHI. During the day, the spatial extension of the SUHI reaches its maximum level and is delimited by the isotherm of 31.0 °C, which is 1.5–2.0 °C warmer than the neighboring non-urban areas. During the night, the SUHI has a more regulated spatial extension around the central area of the city, delimited by the 15.5 °C isotherm with LST values that are 1.0–1.5 °C warmer than the surrounding non-urban areas. Additionally, from a methodological point of view, we highlight that resampled MODIS and Landsat images at a spatial resolution of 500 m can be used with confidence to understand the detailed spatial features of the SUHI. The results of this study could help the elaboration of future policies meant to mitigate the effects of urbanization on the SUHI in an era of increasing air temperatures during summer.
Blind Adaptive Beamforming for a Global Navigation Satellite System Array Receiver Based on Direction Lock Loop
Authors: Wu Jian , Tang Xiaomei , Huang Long , Ni Shaojie , Wang Feixue
Published: July 2023

The adaptive beamforming algorithm can realize interference suppression and navigation signal enhancement, and has been widely used. However, achieving high-precision real-time estimation of the direction of arrival (DOA) parameters of navigation signals in strong-interference scenarios with low complexity is still a challenge. In this paper, a blind adaptive beamforming algorithm for a Global Navigation Satellite System (GNSS) array receiver based on direction lock loop is proposed without using the prior information of the DOA parameter. The direction lock loop is used for DOA tracking and estimation after interference suppression, which uses the spatial correlation of the array beam pattern to construct a closed direction-tracking loop. The DOA estimation value is adjusted in real time based on the loop errors. A blind beamformer is constructed using the DOA estimation results to provide gain by forming a beam in the satellite direction. This method improves the accuracy and dynamic adaptability of DOA estimation while significantly reducing the computational complexity. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.
Deforestation as the Prominent Driver of the Intensifying Wildfire in Cambodia, Revealed through Geospatial Analysis
Authors: Sim Min-Sung , Wee Shi-Jun , Alcantara Enner , Park Edward
Published: July 2023

Cambodia has the most fires per area in Southeast Asia, with fire activity have significantly increased since the early 2000s. Wildfire occurrences are multi-factorial in nature, and isolating the relative contribution of each driver remains a challenge. In this study, we quantify the relative importance of each driver of fire by analyzing annual spatial regression models of fire occurrence across Cambodia from 2003 to 2020. Our models demonstrated satisfactory performance, explaining 69 to 81% of the variance in fire occurrence. We found that deforestation was consistently the dominant driver of fire across 48 to 70% of the country throughout the study period. Although the influence of low precipitation on fires has increased in 2019 and 2020, the period is not long enough to establish any significant trends. During the study period, wind speed, elevation, and soil moisture had a slight influence of 6–20% without any clear trend, indicating that deforestation continues to be the main driver of fire. Our study improves the current understanding of the drivers of biomass fires across Cambodia, and the methodological framework developed here (quantitative decoupling of the drivers) has strong potential to be applied to other fire-prone areas around the world.
Spatial and Temporal Variations of Atmospheric CH4 in Monsoon Asia Detected by Satellite Observations of GOSAT and TROPOMI
Authors: Song Hao , Sheng Mengya , Lei Liping , Guo Kaiyuan , Zhang Shaoqing , Ji Zhanghui
Published: July 2023

Space-based measurements, such as the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, provide global observations of the column-averaged CH4 concentration (XCH4). Due to the irregular observations and data gaps in the retrievals, studies on the spatial and temporal variations of regional atmospheric CH4 concentrations are limited. In this paper, we mapped XCH4 data over monsoon Asia using GOSAT and TROPOMI observations from April 2009 to December 2021 and analyzed the spatial and temporal pattern of atmospheric CH4 variations and emissions. The results show that atmospheric CH4 concentrations over monsoon Asia have long-term increases with an annual growth rate of roughly 8.4 ppb. The spatial and temporal trends of XCH4 data are significantly correlated with anthropogenic CH4 emissions from the bottom-up emission inventory of EDGAR. The spatial pattern of gridded XCH4 temporal variations in China presents a basically consistent distribution with the Heihe–Tengchong Line, which is mainly related to the difference in anthropogenic emissions in the eastern and western areas. Using the mapping of XCH4 data from 2019 to 2021, this study further revealed the response of atmospheric CH4 concentrations to anthropogenic emissions in different urban agglomerations. For the urban agglomerations, the triangle of Central China (TCC), the Chengdu–Chongqing City Group (CCG), and the Yangtze River Delta (YRD) show higher CH4 concentrations and emissions than the Beijing–Tianjin–Hebei region and nearby areas (BTH). The results reveal the spatial and temporal distribution of CH4 concentrations and quantify the differences between urban agglomerations, which will support further studies on the drivers of methane emissions.
Choice of Solar Spectral Irradiance Model for Current and Future Remote Sensing Satellite Missions
Authors: Li Fuqin , Jupp David L. B. , Markham Brian L. , Lau Ian C. , Ong Cindy , Byrne Guy , Thankappan Medhavy , Oliver Simon , Malthus Tim , Fearns Peter
Published: July 2023

The accuracy of surface reflectance estimation for satellite sensors using radiance-based calibrations can depend significantly on the choice of solar spectral irradiance (or solar spectrum) model used for atmospheric correction. Selecting an accurate solar spectrum model is also important for radiance-based sensor calibration and estimation of atmospheric parameters from irradiance observations. Previous research showed that Landsat 8 could be used to evaluate the quality of solar spectrum models. This paper applies the analysis using five previously evaluated and three more recent solar spectrum models using both Landsat 8 (OLI) and Landsat 9 (OLI2). The study was further extended down to 10 nm resolution and a wavelength range from Ultraviolet A (UVA) to shortwave infrared (SWIR) (370–2480 nm) using inversion of field irradiance measurements. The results using OLI and OLI2 as well as the inversion of irradiance measurements were that the more recent Chance and Kurucz (SA2010), Meftah (SOLAR-ISS) and Coddington (TSIS-1) models performed better than all of the previous models. The results were illustrated by simulating dark and bright surface reflectance signatures obtained by atmospheric correction with the different solar spectrum models. The results showed that if the SA2010 model is assumed to be the “true” solar irradiance, using the TSIS-1 or the SOLAR-ISS model will not significantly change the estimated ground reflectance. The other models differ (some to a large extent) in varying wavelength areas.
Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios
Authors: Pan Chaofan , Li Runsheng , Hu Qing , Niu Chaoyang , Liu Wei , Lu Wanjie
Published: July 2023

Fine-grained classification of ship targets is an important task in remote sensing, having numerous applications in military reconnaissance and sea surveillance. Due to the influence of various imaging factors, ship targets in remote sensing images have considerable inter-class similarity and intra-class difference, which brings significant challenges to fine-grained classification. In response, we developed a contrastive learning network based on causal attention (C2Net) to improve the model’s fine-grained identification ability from local details. The asynchronous feature learning mode of “decoupling + aggregation” is adopted to reduce the mutual influence between local features and improve the quality of local features. In the decoupling stage, the feature vectors of each part of the ship targets are de-correlated using a decoupling function to prevent feature adhesion. Considering the possibility of false associations between results and features, the decoupled part is designed based on the counterfactual causal attention network to enhance the model’s predictive logic. In the aggregation stage, the local attention weight learned in the decoupling stage is used to carry out feature fusion on the trunk feature weight. Then, the proposed feature re-association module is used to re-associate and integrate the target local information contained in the fusion feature to obtain the target feature vector. Finally, the aggregation function is used to complete the clustering process of the target feature vectors and fine-grained classification is realized. Using two large-scale datasets, the experimental results show that the proposed C2Net method had better fine-grained classification than other methods.
A Novel Technique for High-Precision Ionospheric VTEC Estimation and Prediction at the Equatorial Ionization Anomaly Region: A Case Study over Haikou Station
Authors: Wang Hai-Ning , Zhu Qing-Lin , Dong Xiang , Sheng Dong-Sheng , Zhi Yong-Feng , Zhou Chen , Xu Bin
Published: July 2023

This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.
Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest
Authors: Arhab Muhammad , Huang Jingshui
Published: July 2023

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model’s performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.
Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique
Authors: Khaliq Fazli , Shabir Muhammad , Khan Inayat , Ahmad Shafiq , Usman Muhammad , Zubair Muhammad , Huda Shamsul
Published: July 2023

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.
Scalability of Cyber-Physical Systems with Real and Virtual Robots in ROS 2
Authors: Mañas-Álvarez Francisco José , Guinaldo María , Dormido Raquel , Dormido-Canto Sebastian
Published: July 2023

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system’s performance when the number of agents increases. To this end, two different frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots, such as the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in experiences with real and virtual robots regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.
A Novel Extraction Procedure of Contact Characteristic Parameters from Current–Voltage Curves in CdZnTe and CdTe Detectors
Authors: Principato Fabio , Bettelli Manuele , Zappettini Andrea , Abbene Leonardo
Published: July 2023

The estimation of the characteristic parameters of the electrical contacts in CdZnTe and CdTe detectors is related to the identification of the main transport mechanisms dominating the currents. These investigations are typically approached by modelling the current–voltage (I–V) curves with the interfacial layer–thermionic-diffusion (ITD) theory, which incorporates the thermionic emission, diffusion and interfacial layer theories into a single theory. The implementation of the ITD model in measured I–V curves is a critical procedure, requiring dedicated simplifications, several best fitting parameters and the identification of the voltage range where each transport mechanism dominates. In this work, we will present a novel method allowing through a simple procedure the estimation of some characteristic parameters of the metal–semiconductor interface in CdZnTe and CdTe detectors. The barrier height and the effects of the interfacial layer will be evaluated through the application of a new function related to the differentiation of the experimental I–V curves.
Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields
Authors: Zheng Li , Yu Wenjie , Li Guangxu , Qin Guangxu , Luo Yunchuan
Published: July 2023

Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.
Progress in Evaluation of Deep Artificial Defects from Sweep-Frequency Eddy-Current Testing Signals
Authors: Smetana Milan , Gombarska Daniela , Psenakova Zuzana
Published: July 2023

The article discusses the practical application of the method of electromagnetic non-destructive investigation of austenitic materials. To identify and evaluate deep artificial defects, the sweep-frequency eddy current method with harmonic excitation is used. The objects of interest are the surface electric-discharged machined notches, with a defined geometry, fabricated in a plate with a thickness of 30 mm. An innovative eddy current probe with a separate excitation and detection circuit is used for the investigation. The achieved results clearly demonstrate the robustness and potential of the method, especially for deep defects in thick material. By using the fifth probe in connection with the frequency sweeping of eddy currents, it is possible to reliably detect artificial defects up to 24 ± 0.5 mm deep by using low-frequency excitation signals. An important fact is that the measuring probe does not have to be placed directly above the examined defect. The experimental results achieved are presented and discussed in this paper. The conducted study can serve, for example, as an input database of defect signals with a defined geometry to increase the convergence of learning networks and for the prediction of the geometry of real (fatigue and stress-corrosion) defects.
Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks
Authors: Du Xiuli , Ding Xiaohui , Tao Fan
Published: July 2023

We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model. The introduction of a clock-cycle for hidden units allows the model to learn short-term information from high-frequency update modules while retaining long-term memory from low-frequency update modules, thereby enhancing the model’s ability to capture data patterns. Experimental results demonstrate that the optimized clock-cycle RNN outperforms other network models in extracting the temporal and nonlinear features of network security situations, leading to improved prediction accuracy. Furthermore, our approach has low time complexity and excellent real-time performance, ideal for monitoring large-scale network traffic in sensor networks.
A Procedure for Taking a Remotely Controlled Elevator with an Autonomous Mobile Robot Based on 2D LIDAR
Authors: Palacín Jordi , Bitriá Ricard , Rubies Elena , Clotet Eduard
Published: July 2023

Navigating between the different floors of a multistory building is a task that requires walking up or down stairs or taking an elevator or lift. This work proposes a procedure to take a remotely controlled elevator with an autonomous mobile robot based on 2D LIDAR. The application of the procedure requires ICP matching for mobile robot self-localization, a building with remotely controlled elevators, and a 2D map of the floors of the building detailing the position of the elevators. The results show that the application of the procedure enables an autonomous mobile robot to take a remotely controlled elevator and to navigate between floors based on 2D LIDAR information.
Crack Detecting Method Based on Grid-Type Sensing Networks Using Electrical Signals
Authors: Ahn Ju-Hun , Lee Yong-Chan , Jeong Se-Min , Kim Han-Na , Lee Chang-Yull
Published: July 2023

Cracks have a primary effect on the failure of a structure. Therefore, the development of crack sensors with high accuracy and resolution and cracks detection method are important. In this study, the crack sensors were fabricated, and the crack locations were detected with the electrical signal of the crack sensor. First, a metal grid-type micro-crack sensor based on silver was fabricated. The sensor is made with electrohydrodynamics (EHD) inkjet printing technology, which is well known as the next generation of printed electronics technology. Optimal printing conditions were established through experiments, and a grid sensor was obtained. After that, single cracks and multiple cracks were simulated on the sensor, and electrical signals generated from the sensor were measured. The measured electrical signal tracked the location of the cracks in three steps: simple cross-calculation, interpolation, and modified P-SPICE. It was confirmed that cracks could be effectively found and displayed using the method presented in this paper.
Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study
Authors: Chen Qicheng , Lee Boon Giin
Published: July 2023

Due to the phenomenon of “involution” in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation between heightened stress levels and overall well-being decline. Therefore, monitoring students’ stress levels is crucial for improving their well-being in educational institutions and at home. Previous studies have primarily focused on recognizing emotions and detecting stress using physiological signals like ECG and EEG. However, these studies often relied on video clips to induce various emotional states, which may not be suitable for university students who already face additional stress to excel academically. In this study, a series of experiments were conducted to evaluate students’ stress levels by engaging them in playing Sudoku games under different distracting conditions. The collected physiological signals, including PPG, ECG, and EEG, were analyzed using enhanced models such as LRCN and self-supervised CNN to assess stress levels. The outcomes were compared with participants’ self-reported stress levels after the experiments. The findings demonstrate that the enhanced models presented in this study exhibit a high level of proficiency in assessing stress levels. Notably, when subjects were presented with Sudoku-solving tasks accompanied by noisy or discordant audio, the models achieved an impressive accuracy rate of 95.13% and an F1-score of 93.72%. Additionally, when subjects engaged in Sudoku-solving activities with another individual monitoring the process, the models achieved a commendable accuracy rate of 97.76% and an F1-score of 96.67%. Finally, under comforting conditions, the models achieved an exceptional accuracy rate of 98.78% with an F1-score of 95.39%.
Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment
Authors: Vets Nieke , De Groef An , Verbeelen Kaat , Devoogdt Nele , Smeets Ann , Van Assche Dieter , De Baets Liesbet , Emmerzaal Jill
Published: July 2023

(1) Background: Being able to objectively assess upper limb (UL) dysfunction in breast cancer survivors (BCS) is an emerging issue. This study aims to determine the accuracy of a pre-trained lab-based machine learning model (MLM) to distinguish functional from non-functional arm movements in a home situation in BCS. (2) Methods: Participants performed four daily life activities while wearing two wrist accelerometers and being video recorded. To define UL functioning, video data were annotated and accelerometer data were analyzed using a counts threshold method and an MLM. Prediction accuracy, recall, sensitivity, f1-score, ‘total minutes functional activity’ and ‘percentage functionally active’ were considered. (3) Results: Despite a good MLM accuracy (0.77–0.90), recall, and specificity, the f1-score was poor. An overestimation of the ‘total minutes functional activity’ and ‘percentage functionally active’ was found by the MLM. Between the video-annotated data and the functional activity determined by the MLM, the mean differences were 0.14% and 0.10% for the left and right side, respectively. For the video-annotated data versus the counts threshold method, the mean differences were 0.27% and 0.24%, respectively. (4) Conclusions: An MLM is a better alternative than the counts threshold method for distinguishing functional from non-functional arm movements. However, the abovementioned wrist accelerometer-based assessment methods overestimate UL functional activity.
Pulse Oximetry Based on Quadrature Multiplexing of the Amplitude Modulated Photoplethysmographic Signals
Authors: Koseeyaporn Jeerasuda , Wardkein Paramote , Sinchai Ananta , Kainan Pattana , Tuwanut Panwit
Published: July 2023

In this research, a pulse oximeter based on quadrature multiplexing of AM-PPG signals is proposed. The oximeter is operated by a microcontroller and employs a simple amplitude modulation technique to mitigate noise interference during SpO2 measurement. The two AM-PPG signals (RED and IR) are quadrature multiplexed using carrier signals with equal frequencies but a 90-degree phase difference. The study focused on noise interference caused by light intensity and hand movement. The experiment was conducted under three different levels of light intensity: 200 Lux, 950 Lux, and 2200 Lux. For each light intensity level, the SpO2 level was measured under three scenarios: hand still, shadow movement over the hand, and hand shaking. A comparison between the proposed technique and the conventional method reveals that the proposed technique offers a superior performance. The relative error of the measured SpO2 level using the proposed technique was less than 3.1% overall. Based on the study, the proposed technique is less affected by noise interference caused by light intensity and hand movement compared to the conventional method. In addition, the proposed technique has an advantage over contemporary methods in terms of computational complexity. Consequently, the proposed technique can be applied to wearable devices that include SpO2 measurement functionality.
An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation
Authors: Gómez-Cárdenes Óscar , Marichal-Hernández José Gil , Son Jung-Young , Pérez Jiménez Rafael , Rodríguez-Ramos José Manuel
Published: July 2023

In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder–decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method’s processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.
A Multichannel Fluorescent Array Sensor for Discrimination of Different Types of Drug-Induced Kidney Injury
Authors: Sun Kunhui , Wang Bing , Lin Jiaoli , Han Lei , Li Meifang , Wang Ping , Yu Xiean , Tian Jiangwei
Published: July 2023

The differences in urinary proteins could provide a novel opportunity to distinguish the different types of drug-induced kidney injury (DIKI). In this research, Au nanoparticles–polyethyleneimine (AuNPs–PEI) and the three fluorophore-labeled proteins (FLPs) have been constructed as a multichannel fluorescent array sensor via electrostatic interaction, which was used to detect the subtle changes in urine collected from the pathological state of DIKI. Once the urine from different types of DIKI was introduced, the binding equilibrium between AuNPs–PEI and FLPs would be broken due to the competitive binding of urinary protein, and the corresponding fluorescence response pattern would be generated. Depending on the different fluorescence response patterns, the different types of DIKI were successfully identified by principal component analysis (PCA) and linear discriminant analysis (LDA). Accordingly, the strategy was expected to be a powerful technique for evaluating the potential unclear mechanisms of nephrotoxic drugs, which would provide a promising method for screening potential renal-protective drugs.
A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature
Authors: Mak T. H. Alex , Liang Ruixin , Chim T. W. , Yip Joanne
Published: July 2023

The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are detected. However, the current method of spinal examination mostly relies on two-dimensional static imaging, which does not provide real-time information on dynamic spinal behaviour. Therefore, this study explored an easier and more efficient method based on machine learning and sensors to determine the curvature of the spine. Fifteen participants were recruited and performed tests to generate data for training a neural network. This estimated the spinal curvature from the readings of three inertial measurement units and had an average absolute error of 0.261161 cm.
An Overview of Cognitive Radio Technology and Its Applications in Civil Aviation
Authors: Zheng Ruikang , Li Xuan , Chen Yudong
Published: July 2023

This paper provides an overview of cognitive radio technology and its applications in the field of civil aviation. Cognitive radio technology is a relatively new and emerging field that allows for dynamic spectrum access and efficient use of spectrum resources. In the context of civil aviation, cognitive radio technology has the potential to enable more efficient use of the limited radio spectrum available for communication and navigation purposes. This paper examines the current state of cognitive radio technology, including ongoing research and development efforts, regulatory issues, and potential challenges to widespread adoption. The potential applications of cognitive radio technology in civil aviation are also explored, including improved spectrum utilization, increased safety and security, and enhanced situational awareness. Finally, the paper concludes with a discussion of future research directions and the potential impact of cognitive radio technology on the future of civil aviation. It is hoped that this paper will serve as a useful resource for researchers, engineers, and policy makers interested in the emerging field of cognitive radio technology and its potential applications in the field of civil aviation.
The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems
Authors: de Alteriis Giorgio , Ruggiero Davide , Del Prete Francesco , Conte Claudia , Caputo Enzo , Bottino Verdiana , Carone Fabiani Filippo , Accardo Domenico , Schiano Lo Moriello Rosario
Published: July 2023

In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.
Chromobacterium Violaceum: A Model for Evaluating the Anti-Quorum Sensing Activities of Plant Substances
Authors: Dimitrova Petya D. , Damyanova Tsvetozara , Paunova-Krasteva Tsvetelina
Published: July 2023

In the new antibiotic era, the exponential increase in multiresistant bacterial strains has become the main global health problem. Many researchers have focused their efforts on exploring novel or combined strategies for combating bacterial resistance. Good knowledge of the molecular mechanisms of resistance and bacterial virulence factors as key targets provides us with a good basis for resolving the problem. One particularly attractive and promising strategy is to attack the main regulatory “network” of bacterial virulence determinants known as quorum sensing (QS). The inhibition of QS signals will be a novel means of screening more effective quorum-sensing inhibitors (QSIs) and will play a key role in the use of next-generation antimicrobials in the battle against resistance. This motivated the present review to provide a comprehensive clarification of the regulatory mechanisms of quorum-sensing signaling pathways in Chromobacterium violaceum and the discovery of potential plant quorum-sensing inhibitors.
Dropping Out or Continuing Playing—A Case Study of Adolescent’s Motives for Participation in Football
Authors: Bergin Jostein , Lagestad Pål
Published: July 2023

The purpose of this study was to investigate dropout and continuation motives among boys in youth football. Semi-structured interviews were conducted with all players from a former football team, consisting of 13 young people who were 17 years old when the in-depth interviews were conducted. Short interviews with the same players five years earlier (at the age of 12) were also included as part of the data. This strategy was used to gain a longitudinal perspective and a better insight into the participants’ experience of organized football. Including in-depth data from all players at a football team—both players that dropped out and continued playing football, and also including some short interviews data from a longitudinal perspective, this study bring new findings into the discussion about sport participation. When the in-depth interviews were conducted, six of the participants had dropped out of football, while seven were still active. The results show that social factors, ambition, other interests and differences in skill and physical development, were all reasons for dropout from this team. Among those who continued playing football, the social aspect of football, their love for the sport, competitive instinct and the training benefits the sport provided, were reasons they continued playing. Furthermore, the findings showed that those who reported the lowest ambitions at the age of twelve, had dropped out of football, while the one with the highest ambitions had continued playing football. Our findings indicate that ambition can be a crucial factor in relation to participation in football, and also that the social aspect of football is an important motivational factor that coaches and parents should be aware of.
Factors Influencing Innovation Performance in Portugal: A Cross-Country Comparative Analysis Based on the Global Innovation Index and on the European Innovation Scoreboard
Authors: Coutinho Evelina Maria Oliveira , Au-Yong-Oliveira Manuel
Published: July 2023

Innovation plays a key role in meeting the challenges of the future, but despite the unprecedented investment in innovation, Portugal has seen a decline in the various indicators that assess the country’s performance. This study aims to answer questions about the state of innovation in Portugal, based on the relevant global and European innovation indicators, comparing the country’s performance with that of Ireland, Belgium, and the Czech Republic. Using secondary data collected from the reports of the last four years, explanatory research was conducted based on statistical and graphical methods in order to establish causal relationships. The areas where the main changes have taken place are presented, highlighting the aspects in which Portugal stands out for superior or poor performance, providing a benchmark for the definition of policies to foster innovation in Portugal. The results demonstrate that institutions, business sophistication, and knowledge and technology score negatively, while creativity stands out as a strength. Environmental sustainability, firms’ investment in innovation, and the impact of innovation on sales are aspects that Portugal needs to improve; human capital and the attractiveness of the R&D system deserve positive remarks. It is fundamental to understand how Portugal is preparing for the future and what the country can learn from others. This study is limited by the specific period in analysis, which could affect causal relationships, and the historical perspective could provide guidelines to the understanding of the relative position of the country. This study contributes new perspectives and knowledge about the state of innovation in Portugal, providing clues to entrepreneurs, policy makers, and the scientific community.
Study of a New Wave Energy Converter with Perturb and Observe Maximum Power Point Tracking Method
Authors: Ugaz Peña José Carlos , Medina Rodríguez Christian Luis , Guarniz Avalos Gustavo O.
Published: July 2023

Ocean waves contain the highest energy density among renewable energy sources. However, harnessing the energy from ocean waves represents a challenge because wave energy converters (WECs) must be designed to have great survivability and efficiency. The power production challenge of any WEC depends on the power take-off (PTO) system efficiency. Maximum power point tracking (MPPT) algorithms have been widely applied in renewable energy from photovoltaic and wind sources, and have subsequently been adapted to wave energy converters (WECs). Energy extraction is optimized by applying MPPT, resulting in an increase in efficiency. This study aims to address the analysis of the influence of the perturb and observe MPPT in the electrical power performance of a WEC composed of a point absorber, a hinged arm and a direct mechanical drive PTO system. The PTO is characterized by a pulley system, a counterweight, one-way bearings, a gearbox, a flywheel and an electric generator; in the present study it is considered to be a cylindrical point absorber. The linear theory and the viscous damping effect are applied to analyze the hydrodynamic behavior of the point absorber. Regarding the two generators considered in the present study, the contribution of MPPT is greater for the low power generator; the high values of the capture width ratio (CWR) occur at low values of period and wave height, showing the maximum value in the high-power generator.
Climate Change Mitigation in Agriculture: Barriers to the Adoption of Carbon Farming Policies in the EU
Authors: Van Hoof Sam
Published: July 2023

Climate change mitigation in the agricultural sector is essential to keep the goal of limiting global warming to 1.5 °C within reach. This article explores why there has been a limited adoption of carbon farming policies in the EU, despite the potential for emissions reductions and carbon sequestration at the farm level. Desk research revealed that EU Member States are increasingly setting sectoral climate targets for agriculture, but there is a lack of policies addressing carbon farming. Governments have largely refrained from using laws and regulatory instruments, with strategies and plans representing the large majority of carbon farming policies in the EU. Moreover, interviews with policymakers and other stakeholders revealed that the main barriers to the adoption of carbon farming policies are concerns over carbon leakage and competitive advantage, the need for a just transition, and structural issues in the food value chain. Despite being regarded by researchers as a main barrier to carbon farming, the agricultural lobby is not perceived as a barrier by policymakers, who emphasise the importance of involving farmers in the policy process. A key implication of these findings is that carbon farming policies need to form part of a wider food system transformation in order to successfully contribute to climate change mitigation.
Progress of Mine Land Reclamation and Ecological Restoration Research Based on Bibliometric Analysis
Authors: Shao Ya , Xu Qinxue , Wei Xi
Published: July 2023

The mining of mineral resources has caused serious damage to land and significant pressure on ecological environment. During the repairing of damaged land and degraded ecosystems, there have been many pieces of literature related to land reclamation and ecological restoration (LRER) that have emerged. To understand the progress and prospect of LRER research, it is necessary to sort out such pieces of literature, analyze the current research status, and forecast the future research directions. Here, Bibliometrix R-package was used to analyze 2357 articles, which were derived from the core database of Web of Science, to explore the development of LRER from 1990 to 2022. The results are as follows. (1) The annual scientific output results show that both the number of articles published on LRER and the number of articles annually citied were increasing gradually from 1990 to 2022. (2) High-frequency keyword analysis indicates that heavy metal (Cd, Pb) pollution remediation is a research hotspot. The cluster analysis (CA) and multiple correspondence analysis (MCA) show that there are two clusters in the current research of LRER, in which one surrounds heavy metal pollution and the other focuses on ecological restoration of mining areas. The two clusters correspond to the remediation and ecological restoration (rehabilitation) stages of stepwise ecological restoration, respectively. Thematic evolution analysis shows that, for more than 30 years, mine drainage and heavy metal pollution treatment, soil reconstruction (soil profile reconstruction, soil improvement), and vegetation restoration have been the focus of research. (3) Future research should focus on the relationship between mine ecological restoration and carbon sequestration and the relationship between ecological restoration and biodiversity in mine areas. In addition, LRER technology exchange, international cooperation, and industrialization are also main directions of development. Generally, in this study, metrology software (Bibliometrix R-package 3.1.4) from the literature was used to sort out the relevant literature on LRER over the past 30 years so as to provide reference for future research on LRER.