bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a Spectral Index for the Detection of Yellow-Flowering Vegetation
Authors: Shao Congying , Shuai Yanmin , Wu Hao , Deng Xiaolian , Zhang Xuecong , Xu Aigong
Published: March 2023

Floral phenology as a special indicator of climate change and vegetation dynamics is drawing more attention. The long-term observations of flowering events collected at scattered ground sites have accumulated valuable priority on the understanding of floral phenology, but with insufficient investigation on the spatio-temporal dynamics at regional scale, which is mainly induced by the lack of effective ways to capture the pixel-based flower events from remote sensing images. The existing yellowness indices are constructed for rape (Brassica napus L.) with less suppression to the bright background and dark green vegetation, and further with inadequate consideration on physiological characteristics and the temporal spectral signature of investigated vegetation. In this paper, we examined rape and several other representative vegetation types to determine spectral features of yellow-flower period within the growing season, then selected the visible and near-infrared bands to construct a Novel Yellowness Index (NYI) with an enhancement on the physiological mechanism of plants. The proposed NYI were discussed on the variation of mathematical properties with representative instances, cross-compared with three typical yellowness indices—Ratio Yellowness Index (RYI), Normalized Difference Yellowness Index (NDYI), and Ashourloo Canola Index (ACI) —over various yellow-flowering vegetation species at multiple scales, and validated with ground observations of three available PhenoCam network stations and field phenological observations at Görlitz, Sachsen, and Germany. In addition, we applied NYI to detect the rape field using Sentinel-2 image at Görlitz with typical rape area as a case study. Results show that the proposed NYI exhibits the potential to capture yellow-flowering events with increased sensitivity to the variation of flower density, and reduction of noise introduced by bright background or dark green vegetation of multiple vegetation species at different scales. As the flower density increases from 33% to 78%, the relative differences of NYI captured can reach up to 74%, compared with other three indices which have the relative differences no more than 57%. The cross-comparison indicates NYI performs better with higher consistent with PhenoCam observation and Deutscher Wetterdienst phenological station than other yellowness indices in capturing the variation of yellow flower density. The case study of NYI application in the identification of rape field exhibits good accuracy with the overall accuracy up to 97.5%, the Kappa coefficient of 0.94, and F score of 0.96. Consequently, the satellite-derived yellowness index will be a potential means to investigate the flowering dynamics and planting range of yellow-flowering vegetation such as rape.
AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos
Authors: Wang Shiqing , Qian Kun , Shen Jianlu , Ma Hongyu , Chen Peng
Published: March 2023

Object tracking using Hyperspectral Images (HSIs) obtains satisfactory result in distinguishing objects with similar colors. Yet, the tracking algorithm tends to fail when the target undergoes deformation. In this paper, a SiamRPN based hyperspectral tracker is proposed to deal with this problem. Firstly, a band selection method based on a genetic optimization method is designed for rapidly reducing the redundancy of information in HSIs. Specifically, three bands with highest joint entropy are selected. To solve the problem that the information of the template in the SiamRPN model decays over time, an update network is trained on the dataset from general objective tracking benchmark, which can obtain effective cumulative templates. The use of cumulative templates with spectral information makes it easier to track the deformed target. In addition, transfer learning of the pre-trained SiamRPN is designed to obtain a better model for HSIs. The experimental results show that the proposed tracker can obtain good tracking results over the entire public dataset, and that it is better than the other popular trackers when the target’s deformation is qualitatively and quantitatively compared, achieving an overall success rate of 57.5% and a deformation challenge success rate of 70.8%.
A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression
Authors: Liang Minggao , Zhang Laifu , Wu Sensen , Zhu Yilin , Dai Zhen , Wang Yuanyuan , Qi Jin , Chen Yijun , Du Zhenhong
Published: March 2023

Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (<120 m), and accordingly limit the representation of regional details and accuracy of temperature inversion. In this study, by integrating normalized difference vegetation index (NDVI), normalized difference building index (NDBI), digital elevation model (DEM), and slope data, a high-resolution surface temperature downscaling method based on geographically neural network weighted regression (GNNWR) was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy (maximum R2 of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.
Learning Sparse Geometric Features for Building Segmentation from Low-Resolution Remote-Sensing Images
Authors: Liu Zeping , Tang Hong
Published: March 2023

High-resolution remote-sensing imagery has proven useful for building extraction. Unfortunately, due to the high acquisition costs and infrequent availability of high-resolution imagery, low-resolution images are more practical for large-scale mapping or change tracking of buildings. However, extracting buildings from low-resolution images is a challenging task. Compared with high-resolution images, low-resolution images pose two critical challenges in terms of building segmentation: the effects of fuzzy boundary details on buildings and the lack of local textures. In this study, we propose a sparse geometric feature attention network (SGFANet) based on multi-level feature fusion to address the aforementioned issues. From the perspective of the fuzzy effect, SGFANet enhances the representative boundary features by calculating the point-wise affinity of the selected feature points in a top-down manner. From the perspective of lacking local textures, we convert the top-down propagation from local to non-local by introducing the grounding transformer harvesting the global attention of the input image. SGFANet outperforms competing baselines on remote-sensing images collected worldwide and multiple sensors at 4 and 10 m resolution, thereby, improving the IoU by at least 0.66%. Notably, our method is robust and generalizable, which makes it useful for extending the accessibility and scalability of building dynamic tracking across developing areas (e.g., the Xiong’an New Area in China) by using low-resolution images.
Cloud Top Thermodynamic Phase from Synergistic Lidar-Radar Cloud Products from Polar Orbiting Satellites: Implications for Observations from Geostationary Satellites
Authors: Mayer Johanna , Ewald Florian , Bugliaro Luca , Voigt Christiane
Published: March 2023

The cloud thermodynamic phase is a crucial parameter to understand the Earth’s radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘C, followed by that for MP at −13 ∘C. Between 0 and −27 ∘C, the occurrence of SC and MP dominates IC, while below −27 ∘C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites.
Large-Scale Estimation of Hourly Surface Air Temperature Based on Observations from the FY-4A Geostationary Satellite
Authors: Zhang Zhenwei , Liang Yanzhi , Zhang Guangxia , Liang Chen
Published: March 2023

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets.
Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
Authors: Yalçinkaya Beril , Couceiro Micael S. , Soares Salviano Pinto , Valente Antonio
Published: March 2023

This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.
Research Progress on Magneto-Refractive Magnetic Field Fiber Sensors
Authors: Wei Linyi , Yu Yang , Wang Dongying , Yao Siyu , Li Ning , Weng Junjie , Zhang Shumao , Liang Jianqiao , Ma Hansi , Yang Junbo , Zhang Zhenrong
Published: March 2023

The magnetic field is a vital physical quantity in nature that is closely related to human production life. Magnetic field sensors (namely magnetometers) have significant application value in scientific research, engineering applications, industrial productions, and so forth. Accompanied by the continuous development of magnetic materials and fiber-sensing technology, fiber sensors based on the Magneto-Refractive Effect (MRE) not only take advantage in compact structure, superior performance, and strong environmental adaptability but also further meet the requirement of the quasi-distributed/distributed magnetic field sensing; they manifest potential and great application value in space detection, marine environmental monitoring, etc. Consequently, the present and prevalent Magneto-Refractive Magnetic Field Fiber Sensors (MR-MFSs) are briefly summarized by this paper, proceeding from the perspective of physicochemical properties; design methods, basic performance and properties are introduced systematically as well. Furthermore, this paper also summarizes key fabrication techniques and future development trends of MR-MFSs, expecting to provide ideas and technical references for staff engaging in relevant research.
Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data
Authors: Duncanson Kayne A. , Thwaites Simon , Booth David , Hanly Gary , Robertson William S. P. , Abbasnejad Ehsan , Thewlis Dominic
Published: March 2023

Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases.
Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review
Authors: Triantafyllidis Andreas , Segkouli Sofia , Zygouris Stelios , Michailidou Christina , Avgerinakis Konstantinos , Fappa Evangelia , Vassiliades Sophia , Bougea Anastasia , Papagiannakis Nikos , Katakis Ioannis , Mathioudis Evangelos , Sorici Alexandru , Bajenaru Lidia , Tageo Valentina , Camonita Francesco , Magga-Nteve Christoniki , Vrochidis Stefanos , Pedullà Ludovico , Brichetto Giampaolo , Tsakanikas Panagiotis , Votis Konstantinos , Tzovaras Dimitrios
Published: March 2023

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson’s disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient’s physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.
Self-Consistent Soundscape Ranking Index: The Case of an Urban Park
Authors: Benocci Roberto , Afify Andrea , Potenza Andrea , Roman H. Eduardo , Zambon Giovanni
Published: March 2023

We have performed a detailed analysis of the soundscape inside an urban park (located in the city of Milan) based on simultaneous sound recordings at 16 locations within the park. The sound sensors were deployed over a regular grid covering an area of about 22 hectares, surrounded by a variety of anthropophonic sources. The recordings span 3.5 h each over a period of four consecutive days. We aimed at determining a soundscape ranking index (SRI) evaluated at each site in the grid by introducing 4 unknown parameters. To this end, a careful aural survey from a single day was performed in order to identify the presence of 19 predefined sound categories within a minute, every 3 minutes of recording. It is found that all SRI values fluctuate considerably within the 70 time intervals considered. The corresponding histograms were used to define a dissimilarity function for each pair of sites. Dissimilarity was found to increase significantly with the inter-site distance in space. Optimal values of the 4 parameters were obtained by minimizing the standard deviation of the data, consistent with a fifth parameter describing the variation of dissimilarity with distance. As a result, we classify the sites into three main categories: “poor”, “medium” and “good” environmental sound quality. This study can be useful to assess the quality of a soundscape in general situations.
A Measurement System for the Environmental Load Assessment of a Scale Ship Model—Part II
Authors: Miller Anna , Rak Andrzej
Published: March 2023

In the process of ship motion control system design, it is necessary to take into account the impact of environmental disturbances such as winds, waves and sea currents. The commonly used representatives of wave influences in this area are the unidirectional wave power spectral density functions describing sea waves of different form: long-crested, fully developed waves, developing wind waves or multi-modal waves (e.g., with swell). The existing standard PSD models describe the surge of open sea or ocean. However, they are inadequate in the case of control system testing of scale ship models for sailing in open water areas such as lakes or test pools. This paper presents a study of wind-generated wave PSD estimations for a small lake used as a test area for free-running scale ships. The publication provides a brief overview of the wave spectral density functions commonly used for control system design. A measurement instrument using the idea of a water-induced variable capacitance that works synchronously with the wind sensors is also described. The process of collected data analysis is presented. As a result of the study, a series of empirical spectral density functions of lake waves for different wind speeds are obtained. They correspond to the rescaled, two-parametric ITTC model.
Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning
Authors: Sun Yi , Sheng Dongfa , Liu Dewen
Published: March 2023

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks’ machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.
A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles
Authors: Tse Kwai-Wa , Pi Rendong , Sun Yuxiang , Wen Chih-Yung , Feng Yurong
Published: March 2023

Traditional methods on crack inspection for large infrastructures require a number of structural health inspection devices and instruments. They usually use the signal changes caused by physical deformations from cracks to detect the cracks, which is time-consuming and cost-ineffective. In this work, we propose a novel real-time crack inspection system based on unmanned aerial vehicles for real-world applications. The proposed system successfully detects and classifies various types of cracks. It can accurately find the crack positions in the world coordinate system. Our detector is based on an improved YOLOv4 with an attention module, which produces 90.02% mean average precision (mAP) and outperforms the YOLOv4-original by 5.23% in terms of mAP. The proposed system is low-cost and lightweight. Moreover, it is not restricted by navigation trajectories. The experimental results demonstrate the robustness and effectiveness of our system in real-world crack inspection tasks.
High-Resolution Swin Transformer for Automatic Medical Image Segmentation
Authors: Wei Chen , Ren Shenghan , Guo Kaitai , Hu Haihong , Liang Jimin
Published: March 2023

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon’s liver dataset, and the BTCV multi-organ segmentation dataset.
A Fully Differential Analog Front-End for Signal Processing from EMG Sensor in 28 nm FDSOI Technology
Authors: Kledrowetz Vilem , Prokop Roman , Fujcik Lukas , Haze Jiri
Published: March 2023

This paper presents a novel analog front-end for EMG sensor signal processing powered by 1 V. Such a low supply voltage requires specific design steps enabled using the 28 nm fully depleted silicon on insulator (FDSOI) technology from STMicroelectronics. An active ground circuit is implemented to keep the input common-mode voltage close to the analog ground and to minimize external interference. The amplifier circuit comprises an input instrumentation amplifier (INA) and a programmable-gain amplifier (PGA). Both are implemented in a fully differential topology. The actual performance of the circuit is analyzed using the corner and Monte Carlo analyses that comprise fifth-hundred samples for the global and local process variations. The proposed circuit achieves a high common-mode rejection ratio (CMRR) of 105.5 dB and a high input impedance of 11 GΩ with a chip area of 0.09 mm2.
Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability
Authors: Suganthi K. , Kumar M. Arun , Harish N. , HariKrishnan S. , Rajesh G. , Reka S. Sofana
Published: March 2023

In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.
Ethanol and NaCl-Induced Gold Nanoparticle Aggregation Toxicity toward DNA Investigated with a DNA/GCE Biosensor
Authors: Blaškovičová Jana , Vyskočil Vlastimil , Augustín Michal , Purdešová Andrea
Published: March 2023

Engineered nanomaterials are becoming increasingly common in commercial and consumer products and pose a serious toxicological threat. Exposure of human organisms to nanomaterials can occur by inhalation, oral intake, or dermal transport. Together with the consumption of alcohol in the physiological environment of the body containing NaCl, this has raised concerns about the potentially harmful effects of ingested nanomaterials on human health. Although gold nanoparticles (AuNPs) exhibit great potential for various biomedical applications, there is some inconsistency in the case of the unambiguous genotoxicity of AuNPs due to differences in their shape, size, solubility, and exposure time. A DNA/GCE (DNA/glassy carbon electrode) biosensor was used to study ethanol (EtOH) and NaCl-induced gold nanoparticle aggregation genotoxicity under UV light in this study. The genotoxic effect of dispersed and aggregated negatively charged gold nanoparticles AuNP1 (8 nm) and AuNP2 (30 nm) toward salmon sperm double-stranded dsDNA was monitored by cyclic and square-wave voltammetry (CV, SWV). Electrochemical impedance spectroscopy (EIS) was used for a surface study of the biosensor. The aggregation of AuNPs was monitored by UV-vis spectroscopy. AuNP1 aggregates formed by 30% v/v EtOH and 0.15 mol·L−1 NaCl caused the greatest damage to the biosensor DNA layer.
Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios
Authors: Jiang Yucheng , Dong Zehua , Mai Songping
Published: March 2023

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.
A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study
Authors: McLean Marnie K. , Weaver R. Glenn , Lane Abbi , Smith Michal T. , Parker Hannah , Stone Ben , McAninch Jonas , Matolak David W. , Burkart Sarah , Chandrashekhar M. V. S. , Armstrong Bridget
Published: March 2023

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. Methods: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). Results: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. Conclusion: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.
Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method
Authors: Raudonis Vidas , Kairys Arturas , Verkauskiene Rasa , Sokolovska Jelizaveta , Petrovski Goran , Balciuniene Vilma Jurate , Volke Vallo
Published: March 2023

In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and IoU values. The ensemble-based model achieved higher Dice score (0.95) and IoU (0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images.
Mass Spectrometry-Based Techniques for the Detection of Non-Intentionally Added Substances in Bioplastics
Authors: Riboni Nicolò , Bianchi Federica , Cavazza Antonella , Piergiovanni Maurizio , Mattarozzi Monica , Careri Maria
Published: March 2023

The safety of food contact materials is a hot topic since chemicals can migrate from packaging into food, thus raising health concerns about and/or producing changes in the organoleptic properties of foodstuffs. Migration tests are required to demonstrate the compliance with current regulations and to investigate the transferred compounds. In this context, mass spectrometry is the analytical technique of choice for the detection and quantitation of both intentionally added substances, such as antioxidants, stabilizers, processing aids, and non-intentionally added substances (NIAS). Untargeted strategies represent a major analytical challenge, providing a comprehensive fingerprinting of the packaging material and migrating components, allowing for NIAS identification. Hyphenated mass spectrometry-based techniques have been devised for screening the presence of migrating contaminants and for quantitation purposes. Both low-resolution (LRMS) and high-resolution (HRMS) methods were screened, with a special emphasis on the latter because of its capability to directly characterize food contact materials with minimal/no sample preparation, avoiding chromatographic separation, and reducing sample handling, analysis costs, and time. Examples related to the migration of contaminants from existing or newly developed bioplastic materials will be discussed, providing an overview of the most used MS-based methods, covering the state-of-the-art approaches from 2012 up to 2022.
Separation Science Tools Serve Forensic Science and Toxicology
Authors: Perrault Katelynn A.
Published: March 2023

The pathway to developing routine tools to investigate evidence in forensic laboratories is a challenging and lengthy process [...]
Analysis of Urinary Amino Acids by High-Performance Liquid Chromatography with Fluorescence Detection Using 2,3-Naphthalenedicarboxaldehyde as Fluorescence Derivatization Reagent
Authors: Ishida Yuma , Inoue Ryoto , Tsunoda Makoto
Published: March 2023

Amino acids are involved in various chemical reactions in vivo, and changes in several amino acids in urine are related to certain disease states. Therefore, developing an efficient method to analyze the amino acids in urine is useful in the timely diagnosis of diseases. In this study, we developed a high-performance liquid chromatography (HPLC) fluorescence method for the quantitative analysis of urinary amino acids using the fluorescence derivatization reagent 2,3-naphthalenedicarboxaldehyde (NDA). NDA was selected because it does not require heating for the reaction and can react within a short time, rendering its use in clinical settings feasible. The reaction temperature, reaction time, and other derivatization conditions were optimized, and the reaction was found to be completed in 5 min at 25 °C. The separation of NDA–amino acids was investigated on an octadecylsilyl (ODS) column under gradient conditions. The mobile phase was a mixture of water–acetonitrile–trifluoroacetic acid. Eighteen NDA–amino acids (histidine (His), arginine (Arg), asparagine (Asn), glutamine (Gln), citrulline (Cit), serine (Ser), aspartic acid (Asp), threonine (Thr), glutamic acid (Glu), glycine (Gly), tyrosine (Tyr), alanine (Ala), tryptophan (Trp), valine (Val), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), and 5-aminovaleric acid (internal standard)) were separated within 100 min under optimal conditions. The calibration curves showed good linearity in the range of 0.25–25 pmol per injection with correlation coefficients of >0.998. The limits of quantification for NDA–amino acids were 16.7–74.7 fmol. The developed analytical method was applied to a human urine sample and 16 amino acids (His, Arg, Asn, Gln, Cit, Ser, Thr, Glu, Gly, Tyr, Ala, Trp, Val, Phe, Ile, and Leu) were quantified. The urinary amino acid concentrations were 5–960 μM. Urinary amino acid analysis using this method is expected to be clinically applicable as a novel biomarker for diseases affecting the bladder, urinary tract, and kidneys.
Association between Community Attachment and Prescription Drug Misuse among American Indian Adolescents in Arizona
Authors: Huang Chao-Kai , Wu Shiyou , Marsiglia Flavio F. , Campos Ana Paola
Published: March 2023

Prescription drug misuse (PDM) has become a major health issue in the U.S. over the past decade. PDM affects all ethnic and racial groups; however, there is a higher prevalence among American Indian (AI) youths, and there is scarce information on the risk and protective factors driving this behavior. Using the Arizona Youth Survey 2018, we analyzed data from 2494 students who self-identified as AI (aged 13–18 years, 47.31% male). Logistic regression models were used to examine the association between community attachment with lifetime and the past-30-days PDM. Community attachment was negatively associated with AI youths’ lifetime PDM (OR = 0.78, 95% CI [0.65, 0.92]); however, it was not significant for the past-30-days users (OR = 0.91, 95% CI [0.72, 1.15]). For both lifetime and past-30-days users, a common protective factor was close friends’ negative perceptions of PDM, while a common risk factor included siblings’ prescription drug use and ease of access to substances. Lifetime users’ drug-free closest friends were also protective. The findings support similar community-oriented approaches showing a cumulative rather than immediate effect, and past-30-days PMD youths were strongly influenced by peers and family. PDM risk and protective factors can advance knowledge about AI youths’ social and cultural determinants of health and influence future prevention interventions.
A Branch and Price Algorithm for the Drop-and-Pickup Container Drayage Problem with Empty Container Constraints
Authors: Song Yujian , Zhang Yuting , Wang Wanli , Xue Ming
Published: March 2023

This paper addresses the drop-and-pickup container drayage problem with empty container constraints. In this problem, a truck is allowed to drop off the container at the customer and then leave. After the container has been packed/unpacked, the truck returns to pick it up. The problem is further complicated by the fact that empty containers at the depot are often limited in number. This container drayage problem is of great practical importance but seldom investigated. In this paper, we first formulate the problem as a directed graph and then mathematically model it as a mixed-integer linear program (MILP) with the objective of minimizing total travel costs. To solve the MILP effectively, we devise a branch and price algorithm that incorporates several performance enhancement strategies, including three versions of the bi-directional label setting algorithm, preprocessing of time windows and a heuristic for high-quality upper bounds. The experimental results indicate that (1) the proposed algorithm significantly outperforms CPLEX in terms of efficiency and effectiveness, (2) an average cost saving of 9.95∼12.25% can be achieved from the drop-and-pickup mode and (3) the benefit of drop-and-pickup mode increases when the customer density and the fixed cost increase.
Non-Monotonic Relationship between Corporate Governance and Banks’ Operating Performance—The Moderating Role of CEO Duality: Evidence from Selected Countries
Authors: Enam Marghoob , Shajar Syed Noorul , Das Niladri
Published: March 2023

This study examines the non-monotonic (U-shaped, inverted U-shaped or curvilinear) relationship between the corporate governance (CG) and bank performance of commercial banks operating across four countries whose CG framework is based on the OECD principals of CG. Using a dataset of 4230 bank-years observation from 2012–2021, the study shows that governance–performance relations may be non-monotonic but not U-shaped using a two-line approach and the Robin Hood algorithm. In addition, this study, using feasible generalized least squares (FGLS), empirically shows that the interaction effect of CEO duality on governance–performance relations in financial institutions is curvilinear and significantly moderates and reverses these impacts. The findings reveal that, in financial institutions with CEO duality, there is a far more modest association between CG and performance, which has an inverted-U shape and is curvilinear. The findings are consistent with arguments advanced by resource dependence and stewardship theory that, although duality might increase bank performance through joint leadership, it can benefit the bank in the presence of unity of command.