bluuebunny/crossref_metadata_2025 huggingface dataset:
Physical and Mechanical Properties of 3D-Printed Provisional Crowns and Fixed Dental Prosthesis Resins Compared to CAD/CAM Milled and Conventional Provisional Resins: A Systematic Review and Meta-Analysis
Authors: Jain Saurabh , Sayed Mohammed E. , Shetty Mallika , Alqahtani Saeed M. , Al Wadei Mohammed Hussain Dafer , Gupta Shilpi Gilra , Othman Ahlam Abdulsalam Ahmed , Alshehri Abdulkarim Hussain , Alqarni Hatem , Mobarki Abdulaziz Hussain , Motlaq Khalid , Bakmani Haifa F. , Zain Asma A. , Hakami Abdullah J. , Sheayria Moayad F.
Published: July 2022

Newly introduced provisional crowns and fixed dental prostheses (FDP) materials should exhibit good physical and mechanical properties necessary to serve the purpose of their fabrication. The aim of this systematic literature review and meta-analysis is to evaluate the articles comparing the physical and mechanical properties of 3D-printed provisional crown and FDP resin materials with CAD/CAM (Computer-Aided Designing/Computer-Aided Manufacturing) milled and conventional provisional resins. Indexed English literature up to April 2022 was systematically searched for articles using the following electronic databases: MEDLINE-PubMed, Web of Science (core collection), Scopus, and the Cochrane library. This systematic review was structured based on the guidelines given by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The focused PICO/PECO (Participant, Intervention/exposure, Comparison, Outcome) question was: ‘Do 3D-printed (P) provisional crowns and FDPs (I) have similar physical and mechanical properties (O) when compared to CAD/CAM milled and other conventionally fabricated ones (C)’. Out of eight hundred and ninety-six titles, which were recognized after a primary search, twenty-five articles were included in the qualitative analysis, and their quality analysis was performed using the modified CONSORT scale. Due to the heterogeneity of the studies, only twelve articles were included for quantitative analysis. Within the limitations of this study, it can be concluded that 3D-printed provisional crown and FDP resin materials have superior mechanical properties but inferior physical properties compared to CAD/CAM milled and other conventionally fabricated ones. Three-dimensionally printed provisional crowns and FDP materials can be used as an alternative to conventional and CAD/CAM milled long-term provisional materials.
A “Square Box”-Structured Triboelectric Nanogenerator for Road Transportation Monitoring
Authors: Chen Zhuo , Wu Hanyi , Xia Zhike , Zou Jian , Wang Shengji , Feng Peiyong , Liu Yuejun , Zhang Zhi , Shang Yinghui , Jing Xin
Published: July 2022

Nowadays, with the rapid development of e-commerce, the transportation of products has become more and more frequent. However, how to monitor the situation of products effectively and conveniently during road transportation is a long-standing problem. In order to meet this problem in practical applications, we fabricated a triboelectric nanogenerator sensor with a “square box” structure (S-TENG) for detecting the vibration suffered by vehicles. Specifically, with the spring installed in the S-TENG as a trigger, the two friction layers can contact and then separate to generate the real-time electrical signals when the S-TENG receives external excitation. The output voltage signals of the S-TENG under different vibration states were tested and the results demonstrated that the peak and zero positions of the open-circuit voltage–output curve are related to amplitude and frequency, respectively. In addition, the subsequent simulation results, obtained by ANSYS and COMSOL software, were highly consistent with the experimental results. Furthermore, we built a platform to simulate the scene of the car passing through speed bumps, and the difference in height and the number of speed bumps were significantly distinguished according to the output voltage signals. Therefore, the S-TENG has broad application prospects in road transportation.
Synthesis and Phase Behavior of a Platform of CO2-Soluble Functional Gradient Copolymers Bearing Metal-Complexing Units
Authors: Ruiu Andrea , Bouilhac Cécile , Gimello Olinda , Seaudeau-Pirouley Karine , Senila Marin , Jänisch Thorsten , Lacroix-Desmazes Patrick
Published: July 2022

The synthesis and characterization of a platform of novel functional fluorinated gradient copolymers soluble in liquid and supercritical CO2 is reported. These functional copolymers are bearing different types of complexing units (pyridine, triphenylphosphine, acetylacetate, thioacetate, and thiol) which are well-known ligands for various metals. They have been prepared by reversible addition–fragmentation chain-transfer (RAFT) polymerization in order to obtain well-defined gradient copolymers. The copolymers have been characterized by proton nuclear magnetic resonance (1H-NMR) spectroscopy, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, thermal gravimetric analysis (TGA), dynamical scanning calorimetry (DSC) and cloud point measurements in dense CO2. All the investigated metal-complexing copolymers are soluble in dense CO2 under mild conditions (pressure lower than 30 MPa up to 65 °C), confirming their potential applications in processes such as metal-catalyzed reactions in dense CO2, metal impregnation, (e.g., preparation of supported catalysts) or metal extraction from various substrates (solid or liquid effluents). Particularly, it opens the door to greener and less energy-demanding processes for the recovery of metals from spent catalysts compared to more conventional pyro- and hydro-metallurgical methods.
Application of Mixing Rules for Adjusting the Flowability of Virgin and Post-Consumer Polypropylene as an Approach for Design from Recycling
Authors: Traxler Ines , Marschik Christian , Farthofer Manuel , Laske Stephan , Fischer Joerg
Published: July 2022

To enable the use of recyclates in thermoformed polypropylene products with acceptable optical appearance and good mechanical stability, a multilayer structure of virgin and recycled material can be used. When producing multilayer films with more than two layers, the used materials should have similar melt flow properties to prevent processing instabilities. In the case of a three-layer film, post-consumer recyclates are often hidden in the core layer. Due to the inconsistent melt flow properties of post-consumer recyclates, the adjustment of the melt flow properties of the core layer to those of the outer layers has to be realized by blending with virgin materials. In order to understand the effect of mixing with a virgin material with a certain pre-defined melt flow rate (MFR), material mixtures with different mixing partners from various sources were realized in this study. Hence, the pre-defined virgin material was mixed with (i) virgin materials, (ii) artificial recyclates out of a mixture of different virgin materials, and (iii) commercially available recyclates. These blends with mixing partner contents ranging from 0–100% in 10% increments were prepared by compounding and the MFR of each mixture was determined. For a mathematical description of the mixing behavior and furthermore for a proper MFR prediction of the material mix, existing mixing rules were tested on the three pre-defined sample groups. Therefore, this paper shows the applicability of different mixing rules for the prediction of the MFR of material blends. Furthermore, a new mixing rule was developed using symbolic regression based on genetic programming, which proved to be the most accurate predictive model.
Does the Application of Additional Hydrophobic Resin to Universal Adhesives Increase Bonding Longevity of Eroded Dentin?
Authors: de Brito Graça Maria Abreu Pereira , Silva Daniella Oliveira , Macedo Rayssa Ferreira Cavaleiro , Ferreira Michel Wendlinger Cantanhede , Bauer Jose , Pedroso Flavia de Brito , Reis Alessandra , Siqueira Fabiana Suelen Figuerêdo , Loguercio Alessandro Dourado , Cardenas Andres Felipe Millan
Published: July 2022

This paper evaluates the effect of an additional hydrophobic resin coat (extra HL) associated with universal adhesives on sound and eroded dentin and evaluated immediately or after 2 years of water storage to improve the microtensile bond strength (μTBS) and nanoleakage (NL) when compared to the use of universal adhesives only. Sixty-four molars were assigned to eight groups using the following combinations: 1. dentin substrate, including sound and eroded dentin; 2. treatment, including the control and extra HL and storage time (immediately and after two-years of storage). Two universal adhesives (Prime & Bond Active or Scotchbond Universal) were evaluated. Before restoration, half of the teeth were subjected to soft-drink erosion. Composite buildups were bonded; specimens were stored (37 °C/24 h), sectioned into resin–dentin bonded sticks and tested for microtensile bond strength and nanoleakage using SEM (immediately and after two-years of storage). Three-way ANOVA and Tukey’s test (α = 0.05%) were used. In the immediate testing, the application of extra HL did not increase microtensile bond strength values compared with the control group in either substrate (p > 0.05). However, extra HL significantly decreased nanoleakage values when applied to eroded and sound dentin (p = 0.0001). After two years, the application of extra HL produced significantly higher microtensile bond strength and lower nanoleakage values than the control group for both adhesives (p = 0.0001). In all cases, sound dentin showed higher microtensile bond strength and lower nanoleakage values than eroded dentin (p = 0.000001). An extra HL increased the bond strength and reduced nanoleakage in eroded dentin after two-years of storage.
Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data
Authors: Lin Shaofu , Zhang Chang , Ding Lei , Zhang Jing , Liu Xiliang , Chen Guihong , Wang Shaohua , Chai Jinchuan
Published: July 2022

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.
Distribution, Magnitude, and Variability of Natural Oil Seeps in the Gulf of Mexico
Authors: O’Reilly Carrie , Silva Mauricio , Daneshgar Asl Samira , Meurer William P. , MacDonald Ian R.
Published: July 2022

The Gulf of Mexico is a hydrocarbon-rich region characterized by the presence of floating oil slicks from persistent natural hydrocarbon seeps, which are reliably captured by synthetic aperture radar (SAR) satellite imaging. Improving the state of knowledge of hydrocarbon seepage in the Gulf of Mexico improves the understanding and quantification of natural seepage rates in North America. We used data derived from SAR scenes collected over the Gulf of Mexico from 1978 to 2018 to locate oil slick origins (OSOs), cluster the OSOs into discrete seep zones, estimate the flux of individual seepage events, and calculate seep recurrence rates. In total, 1618 discrete seep zones were identified, primarily concentrated in the northern Gulf of Mexico within the Louann salt formation, with a secondary concentration in the Campeche region. The centerline method was used to estimate flux based on the drift length of the slick (centerline), the slick area, and average current and wind speeds. Flux estimates from the surface area of oil slicks varied geographically and temporally; on average, seep zones exhibited an 11% recurrence rate, suggesting possible intermittent discharge from natural seeps. The estimated average instantaneous flux for natural seeps is 9.8 mL s−1 (1.9 × 103 bbl yr−1), with an annual discharge of 1.73–6.69 × 105 bbl yr−1 (2.75–10.63 × 104 m3 yr−1) for the entire Gulf of Mexico. The temporal variability of average flux suggests a potential decrease following 1995; however, analysis of flux in four lease blocks indicates that flux has not changed substantially over time. It is unlikely that production activities in the Gulf of Mexico impact natural seepage on a human timescale. Of the 1618 identified seep zones, 1401 are located within U.S. waters, with 70 identified as having flux and recurrence rates significantly higher than the average. Seep zones exhibiting high recurrence rates are more likely to be associated with positive seismic anomalies. Many of the methods developed for this study can be applied to SAR-detected oil slicks in other marine settings to better assess the magnitude of global hydrocarbon seepage.
Trend Changes of the Vegetation Activity in Northeastern East Asia and the Connections with Extreme Climate Indices
Authors: Guo Zijing , Lou Wei , Sun Cheng , He Bin
Published: July 2022

In the context of global warming, vegetation activity in northeastern East Asia (40–45°N, 105–130°E) (NEA) shows a significant growth trend on a multidecadal scale, but how vegetation changes on a decadal scale is unclear. In this study, we find a significant trend of vegetation greening in northeastern East Asia during 1982–1998 and a slowdown in the greening trend during 1998–2014. Trend analysis of the extreme climate indices reveals that the trends of precipitation-related extreme climate indices are similar to those of vegetation change, and further correlation analysis reveals that precipitation-related extreme climate indices have a strong positive correlation with the NDVI. The results indicate that the vegetation in northeastern East Asia is more sensitive to precipitation changes, especially extreme precipitation, compared with the temperature and related extreme indices. Furthermore, the analysis of large-scale atmospheric circulation changes suggests a role of Northwest Pacific subtropical high (NPSH) in the trend changes of precipitation-related extreme indices. The strengthening of NPSH before 1998 enhances the moisture transport to the NEA, providing abundant water vapor favorable for extreme precipitation events, while after 1998, the NPSH trend is much weakened, corresponding to a decrease in the moisture transport trend.
Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features
Authors: He Shan , Peng Peng , Chen Yiyun , Wang Xiaomi
Published: July 2022

Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection (FS), which can availably reduce the number of features by selecting and reserving the most essential features for crop classification, is a good tool to solve this problem effectively. Different FS methods, however, have dissimilar effects on various classifiers, so how to achieve the optimal combination of FS methods and classifiers to meet the needs of high-precision recognition of multiple crops remains an open question. This paper intends to address this problem by coupling the analysis of three FS methods and six classifiers. Spectral, textual, and environmental features are firstly extracted as potential classification indexes from time-series remote sensing images from France. Then, three FS methods are used to obtain feature subsets and combined with six classifiers for coupling analysis. On this basis, 18 multi-crop classification models (FS–ML models) are constructed. Additionally, six classifiers without FS are constructed for comparison. The training set and the validation set for these models are constructed by using the Kennard-Stone algorithm with 70% and 30% of the samples, respectively. The performance of the classification model is evaluated by Kappa, F1-score, accuracy, and other indicators. The results show that different FS methods have dissimilar effects on various models. The best FS–ML model is RFAA+-RF, and its Kappa coefficient can reach 0.7968, which is 0.33–46.67% higher than that of other classification models. The classification results are highly dependent on the original classification index sets. Hence, the reasonability of combining spectral, textural, and environmental indexes is verified by comparing them with the single feature index set. The results also show that the classification strategy combining spectral, textual, and environmental indexes can effectively improve the ability of crop recognition, and the Kappa coefficient is 9.06–65.52% higher than that of the single unscreened feature set.
A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms
Authors: Samee Nagwan Abdel , Alhussan Amel A. , Ghoneim Vidan Fathi , Atteia Ghada , Alkanhel Reem , Al-antari Mugahed A. , Kadah Yasser M.
Published: July 2022

One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.
A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly
Authors: Site Aditi , Vasudevan Saigopal , Afolaranmi Samuel Olaiya , Lastra Jose L. Martinez , Nurmi Jari , Lohan Elena Simona
Published: July 2022

Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.
Estimation of Tibiofemoral Joint Contact Forces Using Foot Loads during Continuous Passive Motions
Authors: Yang Yunlong , Huang Huixuan , Guo Junlong , Yu Fei , Yao Yufeng
Published: July 2022

Continuous passive motion (CPM) machines are commonly used after various knee surgeries, but information on tibiofemoral forces (TFFs) during CPM cycles is limited. This study aimed to explore the changing trend of TFFs during CPM cycles under various ranges of motion (ROM) and body weights (BW) by establishing a two-dimensional mathematical model. TFFs were estimated by using joint angles, foot load, and leg–foot weight. Eleven healthy male participants were tested with ROM ranging from 0° to 120°. The values of the peak TFFs during knee flexion were higher than those during knee extension, varying nonlinearly with ROM. BW had a significant main effect on the peak TFFs and tibiofemoral shear forces, while ROM had a limited effect on the peak TFFs. No significant interaction effects were observed between BW and ROM for each peak TFF, whereas a strong linear correlation existed between the peak tibiofemoral compressive forces (TFCFs) and the peak resultant TFFs (R2 = 0.971, p < 0.01). The proposed method showed promise in serving as an input for optimizing rehabilitation devices.
Three-Dimensional Source Localization with Sparse Symmetric Cross Array
Authors: Wu Haowei , Shi Yiqiao , Ou Jinglan
Published: July 2022

Three-dimensional (3-D) localization information, including elevation angle, azimuth angle, and range, is important for locating a single source with spherical wave-fronts. Aiming to reduce the high computational complexity of the classical 3-D multiple signal classification (3D-MUSIC) localization method, a novel low-complexity reduced-dimension MUSIC (RD-MUSIC) algorithm based on the sparse symmetric cross array (SSCA) is proposed in this article. The RD-MUSIC converts the 3-D exhaustive search into three one-dimensional (1-D) searches, where two of them are obtained by a two-stage reduced-dimension method to find the angles, and the remaining one is utilized to obtain the range. In addition, a detailed complexity analysis is provided. Simulation results demonstrate that the performance of the proposed algorithm is extremely close to that of the existing rank-reduced MUSIC (RARE-MUSIC) and 3D-MUSIC algorithms, whereas the complexity of the proposed method is significantly lower than that of the others, which is a big advantage in practice.
Hot Spot Detection of Photovoltaic Module Based on Distributed Fiber Bragg Grating Sensor
Authors: Li Guoli , Wang Fang , Feng Fei , Wei Bo
Published: July 2022

The hot spot effect is an important factor that affects the power generation performance and service life in the power generation process. To solve the problems of low detection efficiency, low accuracy, and difficulty of distributed hot spot detection, a hot spot detection method using a photovoltaic module based on the distributed fiber Bragg grating (FBG) sensor is proposed. The FBG sensor array was pasted on the surface of the photovoltaic panel, and the drift of the FBG reflected wavelength was demodulated by the tunable laser method, wavelength division multiplexing technology, and peak seeking algorithm. The experimental results show that the proposed method can detect the temperature of the photovoltaic panel in real time and can identify and locate the hot spot effect of the photovoltaic cell. Under the condition of no wind or light wind, the wave number and variation rule of photovoltaic module temperature value, environmental temperature value, and solar radiation power value were basically consistent. When the solar radiation power fluctuated, the fluctuation of hot spot cell temperature was greater than that of the normal photovoltaic cell. As the solar radiation power decreased to a certain value, the temperatures of all photovoltaic cells tended to be similar.
Intelligent Sensors for Human Motion Analysis
Authors: Krzeszowski Tomasz , Switonski Adam , Kepski Michal , Calafate Carlos T.
Published: July 2022

Currently, the analysis of human motion is one of the most interesting and active research topics in computer science, especially in computer vision [...]
NRT-YOLO: Improved YOLOv5 Based on Nested Residual Transformer for Tiny Remote Sensing Object Detection
Authors: Liu Yukuan , He Guanglin , Wang Zehu , Li Weizhe , Huang Hongfei
Published: July 2022

To address the problems of tiny objects and high resolution of object detection in remote sensing imagery, the methods with coarse-grained image cropping have been widely studied. However, these methods are always inefficient and complex due to the two-stage architecture and the huge computation for split images. For these reasons, this article employs YOLO and presents an improved architecture, NRT-YOLO. Specifically, the improvements can be summarized as: extra prediction head and related feature fusion layers; novel nested residual Transformer module, C3NRT; nested residual attention module, C3NRA; and multi-scale testing. The C3NRT module presented in this paper could boost accuracy and reduce complexity of the network at the same time. Moreover, the effectiveness of the proposed method is demonstrated by three kinds of experiments. NRT-YOLO achieves 56.9% mAP0.5 with only 38.1 M parameters in the DOTA dataset, exceeding YOLOv5l by 4.5%. Also, the results of different classifications show its excellent ability to detect small sample objects. As for the C3NRT module, the ablation study and comparison experiment verified that it has the largest contribution to accuracy increment (2.7% in mAP0.5) among the improvements. In conclusion, NRT-YOLO has excellent performance in accuracy improvement and parameter reduction, which is suitable for tiny remote sensing object detection.
Genetic Algorithm and Greedy Strategy-Based Multi-Mission-Point Route Planning for Heavy-Duty Semi-Rigid Airship
Authors: Hu Shaoxing , Wang Bingke , Zhang Aiwu , Deng Yiming
Published: July 2022

The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA.
ECG Classification Using Orthogonal Matching Pursuit and Machine Learning
Authors: Śmigiel Sandra
Published: July 2022

Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.
Empirical Variational Mode Decomposition Based on Binary Tree Algorithm
Authors: Li Huipeng , Xu Bo , Zhou Fengxing , Yan Baokang , Zhou Fengqi
Published: July 2022

Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the α, and the SNR is used as the parameter value of the τ. Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the α and τ need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness.
Affordable Motion Tracking System for Intuitive Programming of Industrial Robots
Authors: Švejda Martin , Goubej Martin , Jáger Arnold , Reitinger Jan , Severa Ondřej
Published: July 2022

The paper deals with a lead-through method of programming for industrial robots. The goal is to automatically reproduce 6DoF trajectories of a tool wielded by a human operator demonstrating a motion task. We present a novel motion-tracking system built around the HTC Vive pose estimation system. Our solution allows complete automation of the robot teaching process. Specific algorithmic issues of system calibration and motion data post-processing are also discussed, constituting the paper’s theoretical contribution. The motion tracking system is successfully deployed in a pilot application of robot-assisted spray painting.
Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters
Authors: Kumar Kundan , Bhaumik Shovan , Arulampalam Sanjeev
Published: July 2022

In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter’s efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement.
Balance and Posture in Children and Adolescents: A Cross-Sectional Study
Authors: Azevedo Nelson , Ribeiro José Carlos , Machado Leandro
Published: July 2022

Balance and posture are two topics that have been extensively studied, although with some conflicting findings. Therefore, the aim of this work is to analyze the relationship between the postural angles of the spine in the sagittal plane and the stable static balance. A cross-sectional study was conducted with children and adolescents from schools in northern Portugal in 2019. An online questionnaire was used to characterize the sample and analyze back pain. Spinal postural angle assessment (pelvic, lumbar, and thoracic) was performed using the Spinal Mouse®, while stabilometry assessment was performed using Namrol® Podoprint®. Statistical significance was set as α = 0.05. The results showed that girls have better balance variables. There is a weak correlation between the anthropometric variables with stabilometry variables and the postural angles. This correlation is mostly negative, except for the thoracic spine with anthropometric variables and the lumbar spine with BMI. The results showed that postural angles of the spine are poor predictors of the stabilometric variables. Concerning back pain, increasing the postural angle of the thoracic spine increases the odds ratio of manifestation of back pain by 3%.
Automated Remote Pulse Oximetry System (ARPOS)
Authors: Pirzada Pireh , Morrison David , Doherty Gayle , Dhasmana Devesh , Harris-Birtill David
Published: July 2022

Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B, and IR data. Moreover, no formal remote photoplethysmography studies have been performed in real-life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from colour, IR, and depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.
System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training
Authors: Rentz Clara , Far Mehran Sahandi , Boltes Maik , Schnitzler Alfons , Amunts Katrin , Dukart Juergen , Minnerop Martina
Published: July 2022

There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach with two standard gait analysis systems (force plate and motion capturing systems). Healthy adults (n = 25, 44.1 ± 18.4 years) completed two laboratory evaluations before and after a three-week gait and balance training at home. There was an excellent agreement between all systems for stride time and cadence during normal, tandem and backward gait, whereas correlations for gait velocity were lower. Balance variables of both standard systems were moderately intercorrelated across all stance tasks, but only few correlated with the corresponding smartphone measures. Significant differences over time were found for several force plate and mocap system-obtained gait variables of normal, backward and tandem gait. Changes in balance variables over time were more heterogeneous and not significant for any system. The smartphone seems to be a suitable method to measure cadence and stride time of different gait, but not balance, tasks in healthy adults. Additional optimizations in data evaluation and processing may further improve the agreement between the analysis systems.
Reliability of 3D Depth Motion Sensors for Capturing Upper Body Motions and Assessing the Quality of Wheelchair Transfers
Authors: Koontz Alicia Marie , Neti Ahlad , Chung Cheng-Shiu , Ayiluri Nithin , Slavens Brooke A. , Davis Celia Genevieve , Wei Lin
Published: July 2022

Wheelchair users must use proper technique when performing sitting-pivot-transfers (SPTs) to prevent upper extremity pain and discomfort. Current methods to analyze the quality of SPTs include the TransKinect, a combination of machine learning (ML) models, and the Transfer Assessment Instrument (TAI), to automatically score the quality of a transfer using Microsoft Kinect V2. With the discontinuation of the V2, there is a necessity to determine the compatibility of other commercial sensors. The Intel RealSense D435 and the Microsoft Kinect Azure were compared against the V2 for inter- and intra-sensor reliability. A secondary analysis with the Azure was also performed to analyze its performance with the existing ML models used to predict transfer quality. The intra- and inter-sensor reliability was higher for the Azure and V2 (n = 7; ICC = 0.63 to 0.92) than the RealSense and V2 (n = 30; ICC = 0.13 to 0.7) for four key features. Additionally, the V2 and the Azure both showed high agreement with each other on the ML outcomes but not against a ground truth. Therefore, the ML models may need to be retrained ideally with the Azure, as it was found to be a more reliable and robust sensor for tracking wheelchair transfers in comparison to the V2.
Embedding Behavioral and Social Sciences across the Medical Curriculum: (Auto) Ethnographic Insights from Medical Schools in the United Kingdom
Authors: Dikomitis Lisa , Wenning Brianne , Ghobrial Andrew , Adams Karen M.
Published: July 2022

Key concepts and theories that are taught in order to develop cultural competency skills are often introduced to medical students throughout behavioral and social science (BSS) learning content. BSS represents a core component of medical education in the United Kingdom. In this paper, we examine, through (auto)ethnographic data and reflections, the experiences of BSS in medical education. The empirical data and insights have been collected in two ways: (1) through long-term ethnographic fieldwork among medical students and (2) via autoethnographic reflexive practice undertaken by the co-authors who studied, worked, examined, and collaborated with colleagues at different UK medical schools. Our findings indicate that despite BSS constituting a mandatory, essential component of the medical curriculum, medical students did not always perceive BSS as useful for their future practice as doctors, nor did they find it to be clinically relevant, in comparison to the biomedical learning content. We suggest that it is paramount for all stakeholders to commit to cultivating and developing cultural competency skills in medical education, through robustly embedding BSS learning content across the undergraduate medical curriculum. We conclude with recommendations for a wide range of educational practices that would ensure a full integration of BSS in the medical curriculum.
Disagreement in the Conceptualization of Educational Quality and Job Satisfaction
Authors: Olaskoaga-Larrauri Jon , Rodríguez-Armenta Carmen Enedina , Marúm-Espinosa Elia
Published: July 2022

The purpose of this paper is to ascertain whether the concepts of quality held by academic staff influences their job satisfaction. The paper defends the thesis that the lack of congruence between the meaning that academics give to quality, on the one hand, and the quality policies implemented by higher education institutions, on the other, generates an (usually latent) conflict that negatively influences the job satisfaction of academics. In addition, this type of disagreement could reduce the effectiveness of quality policies and systems as well as the overall functioning of higher education. Consequently, the identification and measurement of this phenomenon acquires importance. The paper is based on a quantitative research, based on 911 questionnaires from academics in Mexico. Evidence gathered shows that, when academic staff feel that the university where they work does not share their own values and personal convictions about what educational quality should be, it is more likely that they declare job dissatisfaction with their work. The paper also recommends the introduction of variables measuring the symbolic conflict between academic personnel and managers in the models of academics’ job satisfaction in higher education.
Rice Growth Performance, Nutrient Use Efficiency and Changes in Soil Properties Influenced by Biochar under Alternate Wetting and Drying Irrigation
Authors: Haque Ahmad Numery Ashfaqul , Uddin Md. Kamal , Sulaiman Muhammad Firdaus , Amin Adibah Mohd , Hossain Mahmud , Solaiman Zakaria M. , Mosharrof Mehnaz
Published: July 2022

Water-saving irrigation occasionally causes an inconsequential yield loss in rice; thereby, biochar incorporation in this context has great scope due to its properties, including the release of nutrients and improving soil physicochemical properties. An experiment was conducted to investigate the effect of biochar combined with fertilizer on physiological response, water and nutrient efficiency of rice and changes in biochemical properties of soil under AWD (alternate wetting and drying) irrigation system. Two types of irrigation practice, such as AWD and CF (continuous flooding), and four types of fertilizer combination, namely T1: 25% Rice husk biochar (RHB) + 75% of recommended fertilizer dose (RFD); T2: 25% oil palm empty fruit bunch biochar (EFBB) + 75% of RFD; T3: 100% RFD; and T0: 0% biochar and fertilizer, were assigned to assess their impacts. The AWD irrigation produced a sharply reduced grain yield (210.58 g pot−1) compared to CF irrigation (218.04 g pot−1), whereas the biochar combination treatments T1 and T2 produced greater yields (260.27 and 252.12 g pot−1, respectively), which were up to 12.5% higher than RFD. Within AWD, irrigation water usage by T1 and T2 (98.50 and 102.37 g L−1, respectively) was profoundly reduced by up to 28.8%, with improved water use efficiency (WUE). The main effect of biochar treatment T1 and T2 also increased photosynthesis rate during vegetative and maturing stage (up to 17.6 and 24.4%, respectively), in addition to boosting agronomic efficiency of nitrogen (N), phosphorous (P) and potassium (K) compared to RFD (T3). Nevertheless, T1 and T2 significantly enhanced the total carbon and nitrogen; dehydrogenase and urease enzyme activities also increased in both irrigation regimes. The results reveal that the integrated application of RHB and EFBB with fertilizer in the AWD regime significantly reduces irrigation water usage and improves nutrient use efficiency, WUE and soil biochemical properties with a minimum yield penalty for rice.